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DTSTART;TZID=America/Chicago:20221205T090000
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DTSTAMP:20221127T103931
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UID:3584-1670230800-1670511600@www.statscamp.org
SUMMARY:Advanced Structural Equation Modeling: Bayesian SEM
DESCRIPTION:Sponsorship Opportunity:\nApply for $1150 scholarship and learn Bayesian SEM at Stats Camp. Instructor Mauricio Garnier Villarreal got awarded (co-PI) the grant “Scaling Bayesian Latent Variable Models to Big Education Data” from the United States Department of Education. We are able to sponsored 2 students with $1150 each for the registration to the Bayesian SEM course. \n\n\n\n\nTo apply for this sponsorship:\nPlease email the following information to the instructor at mgv@pm.me\n\n\n– CV\n– Explanation on how this course fits your professional development (maximum 300 words)\n– Statement of diversity\, how do you and\or your work is related to underrepresented groups\n\nLIVE STREAM – 4-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview:\nThe goal of the Bayesian SEM is to provide instruction in the application SEM from the Bayesian paradigm. It will cover the application of models commonly implemented in frequentist SEM\, and in models that are complicate or impossible to estimate in the frequentist paradigm. This seminar is design to portray the advantages of the Bayesian paradigm\, both philosophical and practical\, within the application of SEM. The material and examples will be provided in the open source platform R\, but if the students prefer to work with another program we will assist in the understanding and application. In addition\, students who sign up for this seminar can request topics to be included during the weeklong seminar. If we have the expertise\, we’ll gladly prepare and deliver a module on the topic! As with all Stats Camp seminars\, personal consultation time is available and ample support resources are provided on our web pages at statscamp.org. \nA perfect follow up for SEM Foundations or Bayesian Data Analysis! \nSeminar Topics:\n\nBayesian inference\nMCMC estimation algorithm\nPrior selection\nModel comparison\nCommomly applied SEM models: CFA\, SEM\, multiple group\, grorth curve\n\nSeminar Description:\nYou have a good to great understanding of structural equation modeling (SEM)\, maybe you have been doing traditional SEM for years\, but you’ve began a project where your observations are not normally distributed\, you have a small sample size\, or even worse – your model is not converging! What do you do now? Bayesian SEM is your next step! \nAdvanced SEM: Bayesian SEM will cover models that may be too complicated or impossible to estimate in the traditional SEM framework. This seminar highlights the philosophical and practical advantages of the Bayesian approach to SEM. \n\nInstructors: Mauricio Garnier-Villarreal\, Ph.D.\n \nMauricio is a full time assistant professor at Vrije Universiteit Amsterdam. His Ph.D focused on Quantitative Psychology at the University of Kansas completed in the summer of 2016. His research focus is on the application of Bayesian methods to complex structure data for longitudinal analysis\, from both mixed-effects and SEM models. He has experience not only working in the test and development of methods\, but also in the application of these in data; in different fields like special education\, cognitive decline in aging\, healthy aging… (orcid.org/0000-0002-2951-6647). He has been involved in the Stats Camp since 2011. \nRead More\n\n\n\nEsteban Montenegro\, Ph.D.\n \nI’m a researcher at the UC Davis Alzheimer’s Disease Center- East Bay. I conduct data analysis using advanced statistical methods such as latent variable modeling. I have 6 years of experience programming in R\, and I love learning about Linux and statistical new tools.…I’m always open to new projects and ideas\, I collaborate with several teams around the world on topics related to healthy aging\, Alzheimer Disease and other health related topics. \nRead More\n\n\n \n\nAPA Continuing Education Credits:\n \nThis course offers ? hours of Continuing Education Credits. Yhat Enterprises\, LLC is approved by the American Psychological Association to sponsor continuing education for psychologists. Yhat Enterprises\, LLC maintains responsibility for this program and its content. \nSeminar Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\nLearning Objectives\nLearning Objectives:\nAfter engaging in course lectures and discussions as well as completing the hands-on practice activities with real data\, participants will be able to:\n\nDescribe the fundamental properties of Bayesian reasoning and Bayes’ rule.\nDifferentiate between direct probability and indirect probability.\nUtilize Markov Chain Monte Carlo estimation using the Gibbs and Hamiltonian samplers.\nAnalyze Bayesian models in several software packages\, including JAGS\, STAN\, and blavaan.\nIdentify the properties of key distributions used in Bayesian analysis.\nConduct a confirmatory factor analysis using the Bayesian framework.\nImplement prior estimations in conducting CFA modeling.\nEvaluate model fit and compare CFA models.\nModel multiple group CFAs using the Bayesian framework.\nEvaluate latent regression models\, latent interactions\, quadratic effects\, and mediation in a Bayesian framework.\nUse Bayesian methods to evaluate non-normal continuous data.\nConduct SEM for categorical data using Bayesian Item Response Theory.\nAccount for missing data.\nEvaluate longitudinal models in a Bayesian framework.\n\nSeminar Prerequisites\nSeminar Prerequisites:\nRequired: \n\nGeneral understanding of linear regression models\n\nNot required but advantageous: \n\nBasic understanding of Bayesian inference\nBasic understanding of SEM\n\nNo level of proficiency beyond basic awareness is assumed for skills related to: \n\nWorking with R\n\nSoftware and Computer Support\nSoftware and Computer Support:\nParticipants need to bring a laptop computer with Wi-Fi capabilities. \nAll statistical software used at Stats Camp will be available\, free to participants\, on our SMORS (statistical modeling on remote servers) system for the duration of camp.Syllabus\n\n\n\n4-Day Schedule: Bayesian SEM\n\n\nDay 1\n\n\n\n9:00–10:45\nWelcome and Introductions.\n\n\n10:45–11:00\nRest Break\n\n\n11:00–12:30\nBayesian reasoning and Baye’s rule : Direct probability vs indirect probability. Markov Chain Monte Carlo\n\n\n12:30–1:30\nRest Break\n\n\n1:30–3:15\nKnow your distributions. Introduction to blavaan (R)\n\n\n3:15–3:30\nRest Break\n\n\n3:30–5:00\nBayesian Confirmatory Factor Analysis\n\n\nDay 2\n\n\n\n9:00–10:45\nPriors: relevance\, choice\, strengths. CFA with cross-loadings\n\n\n10:45–11:00\nRest Break\n\n\n11:00–12:30\nModel fit and model comparison\n\n\n12:30–1:30\nRest Break\n\n\n1:30–3:15\nMultiple-group CFA\n\n\n3:15–3:30\nRest Break\n\n\n3:30–5:00\nLatent regression models: latent interactions\, and mediation\n\n\nDay 3\n\n\n\n9:00–10:45\nLatent regression models: latent interactions\, and mediation\n\n\n10:45–11:00\nRest Break\n\n\n11:00–12:30\nMissing data handling\n\n\n12:30–1:30\nRest Break\n\n\n1:30–3:15\nSEM for categorical data\n\n\n3:15–3:30\nRest Break\n\n\n3:30–5:00\nLongitudinal CFA (Measurement Invariance)\n\n\n\nDownload Sample Slides and Preview Course Video\nPlease fill out and submit the form below to get instant access to sample course materials.
URL:https://www.statscamp.org/courses/advanced-structural-equation-modeling-bayesian-sem-seminar/
LOCATION:Livestream and/or Asynchronous:\, If you are unavailable to join live via Zoom\, you can participate asynchronously by viewing the recorded course videos for up to 1 year.
CATEGORIES:Advanced Structural Equation Modeling: Bayesian SEM Seminar
ATTACH;FMTTYPE=image/jpeg:https://www.statscamp.org/wp-content/uploads/2022/08/advanced-sem-bayesian-sem-training-seminar.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20221208T090000
DTEND;TZID=America/Chicago:20221211T150000
DTSTAMP:20221127T103931
CREATED:20220908T212013Z
LAST-MODIFIED:20221114T222207Z
UID:4462-1670490000-1670770800@www.statscamp.org
SUMMARY:Longitudinal Structural Equation Modeling (LSEM)
DESCRIPTION:LIVE STREAM – 4-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview:\nThis camp is an advanced intensive short seminar in the analysis of longitudinal data using SEM. The seminar will be a series of lectures and computer workshops to provide participants with advanced training in the use of SEM for the analysis of longitudinal data. \nSeminar Topics:\n\nDesign and measurement issues in cross-sectional and longitudinal research\nTraditional panel designs\nOverview of missing data\nLatent growth curve modeling\nTesting for Mediation and Moderation\nMultilevel and multiple group SEM\nUsing Phantom Constructs\nMultiple group modeling\n\nSeminar Description:\nThe seminar will be a series of lectures and computer workshops to provide participants with advanced training in the use of SEM for the analysis of longitudinal data. \n\nInstructor: Todd D. Little\, Ph.D.\n \nTodd D. Little\, PhD is a Professor and director of the Institute for Measurement\, Methodology\, Analysis and Policy at Texas Tech University. He is widely recognized for his quantitative work on various aspects of applied SEM (e.g.\, modern missing data treatments\, indicator selection\, parceling\, modeling developmental processes) as well as his substantive developmental research (e.g.\, action-control processes and motivation\, coping\, and self-regulation). His work has garnered over 49\,424 …citations with an h-index of 97 and an i10-index of 261. In 2001\, he was elected to membership in the Society for Multivariate Experimental Psychology\, and in 2009\, he was elected President of APA’s Division 5 (Evaluation\, Measurement\, and Statistics). He is a fellow in APA\, APS\, and AAAS. In 2013\, he received the Cohen award from Division 5 of APA for distinguished contributions to teaching and mentoring and in 2015 he received the inaugural distinguished contributions award for mentoring developmental scientists from the Society for Research in Child Development. Both awards cited his founding of Stats Camp (Statscamp.org) in 2003 and its ongoing impact on shaping the quality of scientific inquiry for both past and future generations of researchers. Download Todd’s CV (PDF) \nRead More\n\n\n\nInstructor: Whitney Moore\, Ph.D.\n \nWhitney received her Ph.D. in the Psychosocial Aspects of Health and Physical Activity from the University of Kansas. She is currently an… Assistant Professor at Wayne State University in the Division of Kinesiology\, Health & Sport Studies where she teaches graduate courses in research methods\, sport and exercise psychology\, and statistics. \nRead More\n\n\n\nAPA Continuing Education Credits:\n \nThis course offers ? hours of Continuing Education Credits. Yhat Enterprises\, LLC is approved by the American Psychological Association to sponsor continuing education for psychologists. Yhat Enterprises\, LLC maintains responsibility for this program and its content. \nSeminar Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\nLearning Objectives\nLearning Objectives:\nThis comprehensive 4-day statistics training institute on Longitudinal SEM will enable participants to:\n\nUnderstand the strengths and weaknesses of the different models that can be applied to longitudinal data.\nDevelop a clear understanding of how the models can be specified and adapted to address the specific needs and questions of the investigator.\nGain knowledge of the ways in which one should formulate models test alternative models\, and evaluate models with regard to statistical and practical significance.\n\nSeminar Prerequisites\nSeminar Prerequisites:\nIf you already have a strong background in the application of SEM to analyze the covariance structure of multivariate data and you need to learn how to apply more advanced models to longitudinal data\, this seminar is for you. We strongly recommend that you attend our five-day intensive summer institute on SEM Foundations as a pre-requisite to taking this 4-day advanced seminar. If you have not taken the foundations Seminar\, you should have extensive experience or have taken a graduate-level seminar on SEM before enrolling. \nParticipants from a variety of fields\, including sociology\, psychology\, education\, human development\, marketing\, business\, biology\, medicine\, political science\, and communication\, will benefit from the seminar. \nSoftware and Computer Support\nSoftware and Computer Support:\nParticipants need to bring a laptop computer with Wi-Fi capabilities. \nThe seminar will support LISREL\, Mplus or Laavan. Some assistance will be available for questions related to other structural modeling packages. Previous knowledge of LISREL\, Mplus or Laavan is preferred but not required. \nAll statistical software used at Stats Camp will be available\, free to participants\, on our SMORS (statistical modeling on remote servers) system for the duration of camp.Syllabus\n\n\n\nDay 1\n \n\n\n9:00-10:45\nWelcome and Introductions. Overview of Longitudinal Models\n\n\n10:45-11:00\nSnack and Refreshment Break\n\n\n11:00-12:30\nDesign and Measurement Issues in Longitudinal Modeling\n\n\n12:30-1:30\nLunch Break\n\n\n1:30-3:15\nMissing Data: Planned and Unplanned\n\n\n3:15-3:30\nSnack and Refreshment Break\n\n\n3:30-5:00\nReview of Foundations of SEM\n\n\nDay 2\n\n\n\n\n9:00-10:45\nParcels and Parceling\n\n\n10:45-11:00\nSnacks and Refreshment Break\n\n\n11:00-12:30\nLongitudinal Panel Models: Basics\n\n\n12:30-1:30\nLunch Break\n\n\n1:30-3:15\nMultiple-group Longitudinal Panel Models; CFA and SEM\n\n\n3:15-3:30\nSnack and refreshment Break\n\n\n3:30-5:00\nWrap-up then Individual Consultations\n\n\nDay 3\n\n \n\n\n9:00-10:45\nLongitudinal Mediation\n\n\n10:45-11:00\nSnack and Refreshment Break\n\n\n11:00-12:30\nLongitudinal Moderation\n\n\n12:30-1:30\nLunch Break\n\n\n1:30-3:15\nLatent Growth Curve Modeling: Basics\n\n\n3:15-3:30\nSnack and Refreshment Break\n\n\n3:30-5:00\nLatent Growth Curve Modeling: Multivariate and Multiple Groups\n\n\n\nDownload Sample Slides and Preview Course Video\nPlease fill out and submit the form below to get instant access to sample course materials.
URL:https://www.statscamp.org/courses/longitudinal-structural-equation-modeling-lsem/
LOCATION:Livestream and/or Asynchronous:\, If you are unavailable to join live via Zoom\, you can participate asynchronously by viewing the recorded course videos for up to 1 year.
CATEGORIES:Longitudinal SEM,Longitudinal Structural Equation Modeling
ATTACH;FMTTYPE=image/jpeg:https://www.statscamp.org/wp-content/uploads/2022/09/longitudinal-structural-equation-modeling.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20230109T090000
DTEND;TZID=America/Chicago:20230112T150000
DTSTAMP:20221127T103931
CREATED:20220909T003648Z
LAST-MODIFIED:20221117T170825Z
UID:4477-1673254800-1673535600@www.statscamp.org
SUMMARY:SEM Foundations & Extended Applications
DESCRIPTION:LIVE STREAM – 4-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview: SEM Foundations & Extended Applications\n\nDo you want to take your measurement to the latent level? Well\, this is it\, you have found it\, the foundation to what you need to know for latent variable modeling – structural equation modeling (SEM)! Most campers report their prior training was insufficient and/or outdated. We will introduce you to the current techniques and advances in SEM as well as guide you through the steps to ‘craft’ an exquisite SEM model. \n\nSeminar Topics:\n\nPhantom Constructs\nFitting measurement models\nThree methods of scale setting – including effects coding!\nUpdated recommendations for Scale Validation\nMultiple-Group Comparisons with applications for experimental and observational groups!\nFactorial/Measurement Invariance – Are you measuring the same construct?\nExtended Applications Such as Parceling and Missing Data\nMediation and Indirect Effects using Bootstrapping\nModeration\, creating latent interaction terms!\n\nSeminar Description:\nThis 3 day short course covering SEM Foundations & Extended Applications is an intensive short seminar on the principles of structural equation modeling. \n\nInstructor: Todd D. Little\, Ph.D.\n \nTodd D. Little\, PhD is a Professor and director of the Institute for Measurement\, Methodology\, Analysis and Policy at Texas Tech University. He is widely recognized for his quantitative work on various aspects of applied SEM (e.g.\, modern missing data treatments\, indicator selection\, parceling\, modeling developmental processes) as well as his substantive developmental research (e.g.\, action-control processes and motivation\, coping\, and self-regulation). His work has garnered over 49\,424 …citations with an h-index of 97 and an i10-index of 261. In 2001\, he was elected to membership in the Society for Multivariate Experimental Psychology\, and in 2009\, he was elected President of APA’s Division 5 (Evaluation\, Measurement\, and Statistics). He is a fellow in APA\, APS\, and AAAS. In 2013\, he received the Cohen award from Division 5 of APA for distinguished contributions to teaching and mentoring and in 2015 he received the inaugural distinguished contributions award for mentoring developmental scientists from the Society for Research in Child Development. Both awards cited his founding of Stats Camp (Statscamp.org) in 2003 and its ongoing impact on shaping the quality of scientific inquiry for both past and future generations of researchers. Download Todd’s CV (PDF) \nRead More\n\n\n\nInstructor: Zachary Stickley\, Ph.D.\n \nZachary is a researcher in the College of Education at Texas Tech University studying latent variable modeling and planned missing data. He received his Master of Education degree from Texas Tech University and his Bachelor of Science in Psychology from Tarleton State University.… He has assisted Dr. Little in the instruction of Structural Equation Modeling courses at Texas Tech University as well as at numerous Stats Camp seminars and analysis retreats. \nRead More\n\n\nAPA Continuing Education Credits:\n \nThis course offers ? hours of Continuing Education Credits. Stats Camp Foundation is approved by the American Psychological Association to sponsor continuing education for psychologists. Stats Camp Foundation maintains responsibility for this program and its content. \n\nSeminar Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\nLearning Objectives\nLearning Objectives:\nThe 3-day training institute on Structural Equation Modeling (SEM Foundations) will enable participants to: \n\nDescribe the psychometric properties that underly Structural Equation Modeling (SEM).\nDefine a latent construct using manifest variables.\nIdentify a latent construct using numerous methods of identification\, including marker method\, fixed factor\, and effects coding.\nConduct confirmatory factor analysis (CFA) and evaluate model fit using several fit indices.\nCompare CFA models using several comparison metrics.\nGenerate and implement item parceling schemes.\nEvaluate multiple groups using the CFA framework using weak and strong invariance.\nTest and compare latent parameters in a multiple group framework.\nEvaluate and address missing data with both FIML and Multiple Imputation.\nImplement a planned missing data design.\nEvaluate mediation and moderation in an SEM framework.\nEvaluate multi-trait\, multi-method (MTMM) models.\nEvaluate hierarchical models.\n\nSeminar Prerequisites\nSeminar Prerequisites:\nComing Soon… \nSoftware and Computer Support\nSoftware and Computer Support:\nComing Soon… \nSeminar Audience\nSeminar Audience:\nIf you need to analyze the covariance structure of multivariate data and have a basic statistical background\, this seminar is for you. You should have a good working knowledge of the principles and practice of multiple regression and elementary statistical inference. You do not need to know matrix algebra\, calculus\, or likelihood theory (although that knowledge would be beneficial). Participants from a variety of fields\, including sociology\, psychology\, education\, human development\, marketing\, business\, biology\, medicine\, political science\, and communication\, will benefit from the seminar. \nThe seminar will support LISREL\, Mplus or Laavan. Some assistance will be available for questions related to other structural modeling packages. No previous knowledge of LISREL\, Mplus or Laavan is assumed. Furthermore\, nearly all the techniques taught in the seminar can be translated fairly easily to most other packages. \nSeminar Files\nSeminar Files\nBelow are links to seminar files for those who enrolled in the seminar. Please download these files onto your computer before the first day of the seminar. The files are password protected to respect the intellectual property rights of the instructors. By using your login information you agree not to share your login information or the content protected by it. \nAll statistical software used at Stats Camp will be available\, free to participants\, on our SMORS (statistical modeling on remote servers) system for the duration of camp.Syllabus\n\n\n\nDay 1\n\n \n\n\n1:30 – 3:15\nWelcome and Introductions. Philosophy\n\n\n3:15 – 3:30\nSnack and Refreshment Break\n\n\n3:30 – 5:00\nPsychometrics; Defining Constructs\n\n\n\n\n\n\nDay 2\n\n \n\n\n9:00 – 10:45\nIdentification & Scale setting\n\n\n10:45 – 11:00\nSnack and Refreshment Break\n\n\n11:00 – 12:30\nConfirmatory Factor Analysis I – Introduction to CFA\n\n\n12:30 – 1:30\nLunch Break\n\n\n1:30 – 3:15\nConfirmatory Factor Analysis II – Comparing Models\, Model fit\n\n\n3:15 – 3:30\nSnack and Refreshment Break\n\n\n3:30 – 5:00\nMultiple-Group CFA – Testing for invariance\n\n\n\n\n\n\nDay 3\n\n \n\n\n9:00 – 10:45\nMultiple-Group CFA – Tests and comparing latent parameters\n\n\n10:45 – 11:00\nSnack and Refreshment Break\n\n\n11:00 – 12:30\nParcels and Parceling; Missing Data & Power\n\n\n12:30 – 1:30\nLunch Break\n\n\n1:30 – 3:15\nMultiple-Group SEM & Latent Regression Models\n\n\n3:15 – 3:30\nSnack and Refreshment Break\n\n\n3:30 – 5:00\nCatch up and Discussion\n\n\n\n\n\n\n\nDownload Sample Slides and Preview Course Video\nPlease fill out and submit the form below to get instant access to sample course materials.
URL:https://www.statscamp.org/courses/sem-foundations-and-extended-applications/
LOCATION:Livestream and/or Asynchronous:\, If you are unavailable to join live via Zoom\, you can participate asynchronously by viewing the recorded course videos for up to 1 year.
CATEGORIES:SEM Foundations & Extended Applications
ATTACH;FMTTYPE=image/jpeg:https://www.statscamp.org/wp-content/uploads/2022/09/sem-foundations-training-course.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20230216T090000
DTEND;TZID=America/Chicago:20230219T150000
DTSTAMP:20221127T103931
CREATED:20220701T020125Z
LAST-MODIFIED:20221117T203105Z
UID:2545-1676538000-1676818800@www.statscamp.org
SUMMARY:Structural Equation Modeling (SEM) using R
DESCRIPTION:LIVE STREAM – 4-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview:\nAn introductory 4-day seminar covering SEM with a focus on the statistical software R. \nSeminar Topics:\n\nConfirmatory Factor Analysis\nMultiple group models\nStructural models\nMediation and moderation with latent variables\nLongitudinal models\n\nSeminar Description:\nThis seminar is designed to introduce the theoretical and applied understandings of latent variable models also called SEM. Topics covered include confirmatory factor analysis models\, multiple group models\, mediation\, moderation\, and longitudinal models. Using real datasets provided in the seminar\, participants will learn how to use the lavaan package in the R software program to analyze data and interpret results. Participants from a variety of fields\, including sociology\, psychology\, education\, human development\, marketing\, business\, biology\, medicine\, political science\, and communication\, will benefit from the seminar. \nParticipants will receive an electronic copy of all course materials\, including lecture slides\, practice datasets\, software scripts\, relevant supporting documentation\, and recommended readings. Participants will also have access to a video recording of the course. \n\nInstructor: Alex Schoemann\, Ph.D.\n \nDr. Alexander M. Schoemann\, is an Associate Professor of Psychology at East Carolina University. Alex received his PhD from the University of Kansas in 2011 in Social and Quantitative Psychology under the mentorship of Dr. Kristopher Preacher. He has been a Stats Camp instructor since 2012 (after spending several years as a “counselor”). Alex teaches graduate courses in research design\, regression\, multivariate statistics\, structural equation modeling and multilevel modeling.… His research is focused on applying advanced quantitative methods to data from behavior sciences. Specific topics of interest include mediation and moderation\, power analyses\, missing data estimation\, meta-analysis\, structural equation models and multilevel models. Alex is also interested in developing user friendly software for advanced methods including applications for power analysis for mediation models (http://marlab.org/power_mediation/). \nRead More\n\n\n\nAPA Continuing Education Credits:\n \nThis course offers 16 hours of Continuing Education Credits. Yhat Enterprises\, LLC is approved by the American Psychological Association to sponsor continuing education for psychologists. Yhat Enterprises\, LLC maintains responsibility for this program and its content. \nSeminar Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\n\nLearning Objectives\nLearning Objectives:\nThis comprehensive 3-day statistics training institute on (SEM) using R will enable participants to:\nAfter engaging in course lectures and discussions as well as completing the hands-on practice activities with real data\, participants will be able to: \n\nAcquire an understanding of structural equation modeling techniques as applied in the educational\, social\, health\, and behavioral sciences\nManage and clean data for analysis\nSpecify\, estimate\, evaluate\, and compare different structural equation models using R software\nInterpret and present the results of a structural equation modeling analysis\nCritically evaluate applications of structural equation modeling in scientific studies\nBecome acquainted with structural equation modeling for multiple group and longitudinal data.\n\nParticipants will also complete the course with a foundation for future learning about structural equation modeling and knowledge about available resources to guide such endeavors. \nSeminar Prerequisites\nSeminar Prerequisites:\n– Required:\n\nAdvanced proficiency in multiple linear regression\, including use of categorical independent variables.\nIntermediate fluency with statistical software (e.g. SAS\, SPSS\, or R) which will aid in the use of R (Note that materials for introducing attendees to R software will be shared in advance and the course will begin with a short introduction to R).\n\n– Not required but advantageous:\n\nAt least limited experience (e.g.\, graduate-level course) with multivariate data analysis.\nAt least limited experience using R\n\n\nNo level of proficiency beyond basic awareness is assumed for skills related to:\n\nStructural Equation Modeling.\nAdvanced mathematical or statistical topics such as matrix algebra or likelihood theory.\n\n\n\nSoftware and Computer Support\nSoftware and Computer Support:\nParticipants need a laptop computer with Wi-Fi and webcam capabilities. \nAll statistical software used at Stats Camp will be available\, free to participants\, on our SMORS (statistical modeling on remote servers) system for the duration of camp. \nNote\, however\, that R and RStudio are the software programs that will be demonstrated. Both programs are free and can be downloaded from https://cloud.r-project.org/ and https://www.rstudio.com/products/rstudio/download/\, respectively. We will focus on using the lavaan package\, https://lavaan.ugent.be/\, for analyses. Additional directions will be shared with enrolled participants. \nSyllabus\n\n\n\n\n\nDay 1\n\n\n\n\n9:00-9:30\nWelcome and introductions\n\n\n9:30-10:45\nIntroduction to SEM and Basics of R\n\n\n10:45-11:00\nSnack and refreshments break\n\n\n11:00-12:30\nModel identification and scale setting\n\n\n12:30-1:30\nLunch break\n\n\n1:30-3:00\nConfirmatory Factor Analysis and model fit\n\n\n3:00-3:15\nSnack and refreshments break\n\n\n3:15-5:00\nMultiple group analysis\n\n\nDay 2\n\n\n\n\n9:00-10:45\nMissing data and power\n\n\n10:45-11:00\nSnack and refreshments break\n\n\n11:00-12:30\nStructural Models\, mediation\, and moderation\n\n\n12:30-1:30\nLunch break\n\n\n1:30-3:00\nLongitudinal models\n\n\n3:00-3:15\nSnack and refreshments break\n\n\n3:15-5:00\nLongitudinal Models (continued).\n\n\nDay 3\n\n\n\n\n9:00-10:45\nCategorical indicators\n\n\n10:45-11:00\nSnack and refreshments break\n\n\n11:00-12:30\nMultilevel SEM\n\n\n12:30-1:30\nLunch break\n\n\n1:30-5:00\nOne-on-one Consultations with instructor\n\n\n\n\n\n\nDownload Sample Slides and Preview Course Video\nPlease fill out and submit the form below to get instant access to sample course materials.
URL:https://www.statscamp.org/courses/structural-equation-modeling-sem-using-r/
LOCATION:Livestream and/or Asynchronous:\, If you are unavailable to join live via Zoom\, you can participate asynchronously by viewing the recorded course videos for up to 1 year.
CATEGORIES:Structural Equation Modeling (SEM) using R,Winter Camp
ATTACH;FMTTYPE=image/jpeg:https://www.statscamp.org/wp-content/uploads/2022/06/sem-using-r-statistics-course.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20230216T090000
DTEND;TZID=America/Chicago:20230219T150000
DTSTAMP:20221127T103931
CREATED:20220816T021530Z
LAST-MODIFIED:20221114T222043Z
UID:4246-1676538000-1676818800@www.statscamp.org
SUMMARY:Psychometrics
DESCRIPTION:LIVE STREAM – 4-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview:\nAn introductory 4-day course in the application of psychometrics. Participants should be proficient specific to the material covered in a two-semester graduate-level social science statistics course sequence. \nSeminar Topics:\n\nMeasurement and statistical concepts specific to psychometrics\nScaling\, scaling models & scale development – stimulus\, response and subject centered\nValidity – conceptual and statistical aspects necessary for evidential arguments\nIntroduction to Factor analysis – traditional\, IRT and SEM-based approaches/connections\nReliability – classical and modern approaches to estimation of score reliability\nIntroduction to Item Response Theory\n\nSeminar Description:\nPsychometrics is defined as the science of evaluating the characteristics of tests or other devices designed to measure psychological attributes of people. Tests are broadly defined as devices for measuring ability\, aptitude\, achievement\, attitudes\, interests\, personality\, cognitive functioning\, and mental health. Application of psychometrics to psychology and social/behavioral science constitutes an organized effort to (a) properly use theory-based measurement procedures for the development of tests and other measurement instruments for inter- and intraindividual research designs and (b) incorporate current best practices for applying measurement theory\, item/scale development\, reliability estimation (classical and modern)\, factor analysis/IRT and establishing statistical evidence of score validity through a unified approach. advance knowledge in psychological and sensory processes. Participants will receive an electronic copy of all course materials\, including lecture slides\, practice datasets\, software scripts\, relevant supporting documentation\, and recommended readings. Participants will also have access to a video recording of the course. \n\nInstructor: Larry Price\, Ph.D.\n \nLarry Price\, Ph.d. PStat is the Director of the Data Analytics & Methodology at Texas State University. This university-wide role involves supervising a team of quantitative methodologists in conceptualizing and writing the analytic segments of large-scale competitive grant proposals for funding agencies such as the Department of Education/Institute of Education Sciences\, National Science Foundation\, National Institutes of Health\, National Institute on Standards\, and the Department of Defense in collaboration with interdisciplinary research teams. His research has been funded by agencies such as the Department of Education/Institute of Education Sciences\, National Science Foundation\, National Institutes of Health\, National Institute on Standards\, and the Department of Defense. \nRead More\n\n\nAPA Continuing Education Credits:\n \nThis course offers ? hours of Continuing Education Credits. Yhat Enterprises\, LLC is approved by the American Psychological Association to sponsor continuing education for psychologists. Yhat Enterprises\, LLC maintains responsibility for this program and its content. \nSeminar Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\nLearning Objectives\nLearning Objectives:\n\nAcquire a basic understanding of the role of psychometrics as applied to social and behavioral sciences.\nDevelop a clear understanding of the conceptual and theoretical basis of measurement theories\, models\, and statistical concepts specific to psychometrics.\nAcquire knowledge of how to properly apply psychometric techniques such as scale development\, item analysis/refinement\, score reliability and statistical validity.\nGain knowledge of how to apply factor analysis using traditional and structural equation modeling approaches related to test and scale development and evaluation.\nGain knowledge of how to apply generalizability theory for estimating variance components and score reliability when classical test theory model is inadequate.\nAcquire basic knowledge of how and why to apply item response theory for scaling test data and test development and evaluation.\n\nSeminar Prerequisites\nSeminar Prerequisites:\nRequired: \n\nIntermediate proficiency in basic statistical theory as would be gained in a 1st year graduate course.\n\nNot required but advantageous: \n\nLimited experience (e.g.\, graduate-level course) with classical measurement theory and concepts.\n\nNo level of proficiency beyond basic awareness is assumed for skills related to: \n\nAdvanced mathematical or statistical topics such as matrix algebra.\n\nSoftware and Computer Support\nSoftware and Computer Support:\nParticipants need to bring a laptop computer with Wi-Fi capabilities. Students should have access to IBM SPSS\, version 21.0 or higher and Mplus\, version 7.1 or higher and R. \nAll statistical software used at Stats Camp will be available\, free to participants\, on our SMORS (statistical modeling on remote servers) system for the duration of camp.Syllabus\n\n\n\nDay 1\n \n\n\n9:00-9:30\nWelcome and introductions\n\n\n9:30-10:15\nMeasurement & statistical concepts\n\n\n10:15-10:30\nBreak\n\n\n10:30-11:30\nScaling and scaling models – achievement\, ability\, attitude & perception\n\n\n11:30-12:30\nLunch break\n\n\n12:30-1:30\nTechniques for item and test development\, evaluation & refinement\n\n\n1:30-3:00\nValidity – criterion\, content & construct considerations\n\n\n3:15-5:00\nStatistical aspects of the score validation process\n\n\nDay 2\n\n\n\n9:00-10:00\nFactor analysis – foundations\, types and estimating factor models using exploratory and confirmatory approaches – part 1\n\n\n10:00-11:30\nFactor analysis – a unified model for test theory and application\, estimating factor models using structural equation modeling – part 2\n\n\n11:30-12:30\nLunch break\n\n\n12:30-1:30\nComputer exercises – Common Factor Analysis using traditional algorithms for applied factor analysis – exploratory and confirmatory strategies in test development\n\n\n1:30-3:30\nHigher-order\, Bifactor\, and multidimensionality with computer exercises\n\n\nDay 3\n\n\n\n9:00-10:00\nReliability of test scores – foundations/application of classical test theory; Using/applying structural equation modeling and IRT for score reliability estimation; Rater reliability models\n\n\n10:00-11:00\nContemporary approaches to reliability estimation (factor analysis & IRT)\n\n\n11:00-12:00\nIntroduction to Item Response Theory – theory and applications for applied psychometrics; Relationship to structural equation modeling\n\n\n12:00-1:00\nLunch break\n\n\n1:00-3:00\nComputer exercises – Item Response Theory & Factor Analysis for scale construction and refinement\n\n\n3:15-5:00 *\nIndividual Consultations (optional)\n\n\nDay 4\n\n\n\n9:00-10:30\nIntroduction to Measurement Invariance/Differential Item & Test Functioning – example programs for analyses and interpretation\n\n\n10:30-10:45\nBreak\n\n\n10:45-12:00\nIntroduction to generalizability theory – G-studies and D-studies\n\n\n12:00-1:00\nLunch Break\n\n\n1:00-2:00\nGeneralizability – estimating reliability of rater data\n\n\n2:00-3:00\nNormative scores – development and use\n\n\n\nDownload Sample Slides and Preview Course Video\nPlease fill out and submit the form below to get instant access to sample course materials.
URL:https://www.statscamp.org/courses/psychometrics-training/
LOCATION:Livestream and/or Asynchronous:\, If you are unavailable to join live via Zoom\, you can participate asynchronously by viewing the recorded course videos for up to 1 year.
CATEGORIES:Psychometrics
ATTACH;FMTTYPE=image/jpeg:https://www.statscamp.org/wp-content/uploads/2022/08/psychometrics-training-course.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20230217T090000
DTEND;TZID=America/Chicago:20230220T150000
DTSTAMP:20221127T103931
CREATED:20160316T175245Z
LAST-MODIFIED:20221114T221939Z
UID:618-1676624400-1676905200@www.statscamp.org
SUMMARY:Latent Profile Analysis
DESCRIPTION:LIVE STREAM – 4-day Statistics Short Course\n\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview:\nAn introduction to “person-centered” data analysis. Topics include latent profile analysis (aka\, latent class cluster analysis)\, and modeling predictors and outcomes of latent profile membership. Hands-on practice with Mplus is provided. \nSeminar Topics:\nLatent Profile Analysis (LPA) steps including research questions appropriate for latent profile analysis\, profile (class) enumeration and assessing profile model results (classification quality\, profile homogeneity and separation)\, predicting profile membership with other variables and profile membership predicting outcomes. Practice analyses will be completed to build comfort with syntax and reading of output. We will also cover how to interpret and present the results to maximize audience understanding. \nSeminar Description:\nThis three-day camp is an intensive short seminar in the fundamentals of latent profile analysis (LPA).\nLPA is a type of latent variable model-based finite mixture models that express the overall distribution of one or more continuous variables as a mixture of a finite number of component distributions. In direct applications\, one assumes that the overall population heterogeneity with respect to a set of continuous\, manifest variables is due to the existence of two or more distinct homogeneous subgroups\, or latent profiles\, of individuals. These approaches are often termed “person-centered” analyses in contrast to the “variable-centered” analyses of conventional factor and SEM models. \nThis seminar will introduce participants to the prevailing “best practices” for direct applications of basic latent profile analysis to cross-sectional data\, specifically latent profile analysis (LPA) also known as latent class cluster analysis (LCCA)\, including model assumptions\, specification\, estimation\, evaluation\, selection\, and interpretation. Models that allow for the inclusion of correlates and predictors of latent profile membership as well as distal outcomes of latent profile membership will be presented. The implementation of these models in the most recent version of the Mplus software will be demonstrated and practiced throughout the seminar. \nInstructor: Whitney Moore\, Ph.D.\n \nWhitney received her Ph.D. in the Psychosocial Aspects of Health and Physical Activity from the University of Kansas. She is currently an… Assistant Professor at Wayne State University in the Division of Kinesiology\, Health & Sport Studies where she teaches graduate courses in research methods\, sport and exercise psychology\, and statistics. \nRead More\n\n\nAPA Continuing Education Credits:\n \nThis course offers 16 hours of Continuing Education Credits. Yhat Enterprises\, LLC is approved by the American Psychological Association to sponsor continuing education for psychologists. Yhat Enterprises\, LLC maintains responsibility for this program and its content. \n\nSeminar Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\n\nLearning Objectives\nLearning Objectives:\nThis comprehensive 4-day statistics training institute on Latent Profile Analysis will enable participants to:\n\nAcquire understanding of latent profile analysis techniques as applied in the social and behavioral sciences.\nDevelop an appreciation for the research questions and data best suited for latent profile analysis models and the common pitfalls leading to the misuse of mixture models.\nGain detailed knowledge of current “best practices” for mixture model specification\, estimation\, selection\, evaluation\, comparison\, interpretation\, and presentation.\nUnderstand how latent profile variables may be integrated into a larger (latent) variable system.\nBecome acquainted with a variety of mixture modeling extensions.\nBecome proficient in the use of Mplus for analysis of mixture models.\n\nThis seminar is intended to give participants the knowledge and understanding necessary to identify and effectively execute “person-centered” analysis strategies with continuous variables using Mplus that might be most appropriate for their research questions. The seminar is also intended to provide a foundation for future learning about mixture modeling and resources to guide such endeavors. \nSeminar Prerequisites\nSeminar Prerequisites:\nIf you are interested in learning “person-centered” statistical modeling techniques that can identify unobserved subgroups (latent profiles) characterized by qualitative differences in observed multivariate outcome distributions\, this seminar is for you. You should have a good working knowledge of the principles and practice of multiple regression and elementary statistical inference. You will get the most out of the seminar if you already have experience with binary and multinomial logistic regression. You do not need to know matrix algebra\, likelihood theory\, or SEM\, although that knowledge would be beneficial. No previous knowledge of mixture modeling\, latent class analysis\, latent profile analysis\, or Mplus is assumed. Participants from a variety of fields—including psychology\, education\, human development\, public health\, prevention science\, sociology\, marketing\, business\, biology\, medicine\, political science\, and communication—will benefit from the seminar. \nSoftware and Computer Support\nSoftware and Computer Support:\nParticipants should have a laptop computer. Instruction will be provided for the methods using the most current version of Mplus (base program with mixture add-on or base program with combination add-on). Mplus is available for Windows\, Mac\, and Linux environments (www.statmodel.com). \nParticipants who do not have access to software will be given temporary access to the server that contains fully functioning versions of the recommended software.\nNote: We will also make use of Excel to do various post-processing summaries. \nParticipants will receive an electronic copy of all seminar materials\, including PowerPoint slides\, Mplus scripts\, output files\, relevant supporting documentation\, and recommended readings. \nAll statistical software used at Stats Camp will be available\, free to participants\, on our SMORS (statistical modeling on remote servers) system for the duration of camp.Syllabus\n\n\n\n\n\n\n\n\nDay 1\n \n\n\n9:00-9:30\nWelcome and introductions plus Zoom Orientation\n\n\n9:30-10:30\nOverview of mixture modeling in a general latent variable framework\n\n\n10:30-10:45\nSnack and refreshment break\n\n\n10:45-12:15\nOverview of mixture modeling in a general latent variable framework\n\n\n12:30-1:30\nLunch break\n\n\n1:30-3:15\nOverview of LPA steps\n\n\n3:15-3:30\nSnack and refreshment break\n\n\n3:30-5:00\nIntroduction to Mplus syntax introduction with Latent Profile Analysis (LPA) example\n\n\n5:30~7:30\nSocial “hour” reception for all Stats Campers\n\n\nDay 2\n\n\n\n9:00-9:30\nQ & A\n\n\n9:30-10:45\nLPA class enumeration across variance-covariance structures introduction\n\n\n10:45-11:00\nSnack and refreshment break\n\n\n11:00-12:30\nSyntax and interpretation of output for LPA enumeration across variance-covariance structures\n\n\n12:30-1:30\nLunch break\n\n\n1:30-3:15\nIndividual consultation & Practice of LPA enumeration process\n\n\n3:15-3:30\nSnack and refreshment break\n\n\n3:30-5:00\nIndividual consultation & Review of multinomial logistic regression\n\n\nDay 3\n\n \n\n\n9:00-9:30\nReview of LPA enumeration process and decision-making\n\n\n9:30-10:45\nExamination of Profile homogeneity and separation\n\n\n10:45-11:00\nSnack and refreshment break\n\n\n11:00-12:30\nIntroduction to latent class regression (LCR) with inclusion of predictive covariates\n\n\n12:30-1:30\nLunch break\n\n\n1:30-3:15\nLCR continued with inclusion of distal outcomes\n\n\n3:15-3:30\nSnack and refreshment break\n\n\n3:30-5:00\nIndividual consultation\n\n\n\n\n\nDownload Sample Slides and Preview Course Video\nPlease fill out and submit the form below to get instant access to sample course materials.
URL:https://www.statscamp.org/courses/latent-profile-analysis/
LOCATION:Livestream and/or Asynchronous:\, If you are unavailable to join live via Zoom\, you can participate asynchronously by viewing the recorded course videos for up to 1 year.
CATEGORIES:Latent Profile Analysis,Winter Camp
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20230217T090000
DTEND;TZID=America/Chicago:20230220T150000
DTSTAMP:20221127T103931
CREATED:20220701T020243Z
LAST-MODIFIED:20221114T221811Z
UID:2523-1676624400-1676905200@www.statscamp.org
SUMMARY:Longitudinal Structural Equation Modeling (LSEM)
DESCRIPTION:LIVE STREAM – 4-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview:\nA comprehensive 4-day Stats Camp seminar on Longitudinal Structural Equation Modeling. \nSeminar Topics:\n\nDesign and measurement considerations in longitudinal research\nBuilding and evaluating a longitudinal SEM\nLatent Panel SEMs\nEvaluating longitudinal measurement invariance\nMultiple group longitudinal SEM\nLatent Mediation SEM\nLatent growth curve analysis\nAdditional considerations for longitudinal modeling such as missing data and parceling\n\nSeminar Description:\nThis camp is an advanced intensive short course in the analysis of longitudinal data using SEM. The course includes a series of live lectures along with time for individual and group consultation time to provide participants with the tools needed to use of SEM for the analysis of longitudinal data. If you already have a strong background in the application of SEM to analyze the covariance structure of multivariate data and you need to learn how to apply more advanced models to longitudinal data\, this course is for you. Participants from a variety of fields\, including sociology\, psychology\, education\, human development\, marketing\, business\, biology\, medicine\, political science\, and communication\, will benefit from the course. \nParticipants will receive a link to the course materials by the first day that includes lecture slides\, software example scripts (in Mplus\, lavaan\, and LISREL)\, relevant supporting documentation\, and recommended readings. Participants will receive a link to the course video recording at the end of the camp. \nInstructor: Todd Little\, Ph.D.\n \nTodd D. Little\, PhD is a Professor and director of the Institute for Measurement\, Methodology\, Analysis and Policy at Texas Tech University. He is widely recognized for his quantitative work on various aspects of applied SEM (e.g.\, modern missing data treatments\, indicator selection\, parceling\, modeling developmental processes) as well as his substantive developmental research (e.g.\, action-control processes and motivation\, coping\, and self-regulation). His work has garnered over 49\,424 …citations with an h-index of 97 and an i10-index of 261. In 2001\, he was elected to membership in the Society for Multivariate Experimental Psychology\, and in 2009\, he was elected President of APA’s Division 5 (Evaluation\, Measurement\, and Statistics). He is a fellow in APA\, APS\, and AAAS. In 2013\, he received the Cohen award from Division 5 of APA for distinguished contributions to teaching and mentoring and in 2015 he received the inaugural distinguished contributions award for mentoring developmental scientists from the Society for Research in Child Development. Both awards cited his founding of Stats Camp (Statscamp.org) in 2003 and its ongoing impact on shaping the quality of scientific inquiry for both past and future generations of researchers. Download Todd’s CV (PDF) \nRead More\n\n\nInstructor: Elizabeth Grandfield\, Ph.D.\n \nElizabeth received her Ph.D. in Quantitative Psychology at the University of Kansas. She is currently an Assistant Professor in the Department of Methodology and Statistics at Utrecht University in the Netherlands. Her research focuses on evaluating measurement invariance with an emphasis in longitudinal designs. In areas of applied research\, Elizabeth has been involved in longitudinal children studies at Juniper Gardens as well as a national nursing study at Kansas University Medical Center\, both in Kansas City. She also received… the 2011 Multivariate Software Award\, presented by Peter Bentler and Eric Wu. Elizabeth has been involved in Stats Camp since 2012. \nRead More\n\n\nAPA Continuing Education Credits:\n \nThis course offers 16 hours of Continuing Education Credits. Yhat Enterprises\, LLC is approved by the American Psychological Association to sponsor continuing education for psychologists. Yhat Enterprises\, LLC maintains responsibility for this program and its content. \nSeminar Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\n\nLearning Objectives\nLearning Objectives:\nThis comprehensive 3-day statistics training institute on Longitudinal SEM will enable participants to:\n\nUnderstand the strengths and weaknesses of the different models that can be applied to longitudinal data.\nDevelop a clear understanding of how the models can be specified and adapted to address the specific needs and questions of the investigator.\nGain knowledge of the ways in which one should formulate models\, test alternative models\, and evaluate models with regard to statistical and practical significance.\n\nSeminar Prerequisites\nSeminar Prerequisites:\nRequired: \n\nProficiency in multiple linear regression.\nAt least limited experience (e.g.\, graduate-level course) with continuous latent variable models\, e.g.\, exploratory and confirmatory factor analysis (EFA; CFA) and structural equation modeling (SEM).\nWe strongly recommend that you attend our foundations of SEM as a pre-requisite to taking this advanced course. If you have not taken the foundations course\, you should have extensive experience or have taken a graduate-level course on SEM before enrolling.\nIntermediate proficiency with at least one statistical software package (e.g.\, SPSS\, Stata\, SAS\, R\, LISREL\, Mplus\, etc.).\n\nNot required but advantageous: \n\nAt least limited experience (e.g.\, graduate-level course) with multivariate data analysis.\n\nNo level of proficiency beyond basic awareness is assumed for skills related to: \n\nLongitudinal SEM.\nAdvanced mathematical or statistical topics such as matrix algebra or likelihood theory.\n\nSoftware and Computer Support\nSoftware and Computer Support:\narticipants need a laptop computer with Wi-Fi and webcam capabilities. \nAll statistical software used at Stats Camp will be available\, free to participants\, on our SMORS (statistical modeling on remote servers) system for the duration of camp. \nAll statistical software used at Stats Camp will be available\, free to participants\, on our SMORS (statistical modeling on remote servers) system for the duration of camp.Syllabus\n\n\n\nDay 1\n\n\n\n\n9:00-10:45\nWelcome and Introductions. Overview of Longitudinal Models\n\n\n10:45-11:00\nSnack and Refreshment Break\n\n\n11:00-12:30\nDesign and Measurement Issues in Longitudinal Modeling\n\n\n12:30-1:30\nLunch Break\n\n\n1:30-3:15\nMissing Data: Planned and Unplanned\n\n\n3:15-3:30\nSnack and Refreshment Break\n\n\n3:30-5:00\nReview of Foundations of SEM\n\n\nDay 2\n\n\n\n\n9:00-10:45\nParcels and Parceling\n\n\n10:45-11:00\nSnacks and Refreshment Break\n\n\n11:00-12:30\nLongitudinal Panel Models: Basics\n\n\n12:30-1:30\nLunch Break\n\n\n1:30-3:15\nMultiple-group Longitudinal Panel Models; CFA and SEM\n\n\n3:15-3:30\nSnack and refreshment Break\n\n\n3:30-5:00\nWrap-up then Individual Consultations\n\n\nDay 3\n\n\n\n\n9:00-10:45\nLongitudinal Mediation\n\n\n10:45-11:00\nSnack and Refreshment Break\n\n\n11:00-12:30\nLongitudinal Moderation\n\n\n12:30-1:30\nLunch Break\n\n\n1:30-3:15\nLatent Growth Curve Modeling: Basics\n\n\n3:15-3:30\nSnack and Refreshment Break\n\n\n3:30-5:00\nLatent Growth Curve Modeling: Multivariate and Multiple Groups\n\n\n\nDownload Sample Slides and Preview Course Video\nPlease fill out and submit the form below to get instant access to sample course materials.
URL:https://www.statscamp.org/courses/longitudinal-structural-equation-modeling/
LOCATION:Livestream and/or Asynchronous:\, If you are unavailable to join live via Zoom\, you can participate asynchronously by viewing the recorded course videos for up to 1 year.
CATEGORIES:Longitudinal SEM,Longitudinal Structural Equation Modeling,Winter Camp
ATTACH;FMTTYPE=image/jpeg:https://www.statscamp.org/wp-content/uploads/2022/06/longitudinal-structural-equation-modeling-training-course.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20230217T090000
DTEND;TZID=America/Chicago:20230220T150000
DTSTAMP:20221127T103931
CREATED:20220701T022918Z
LAST-MODIFIED:20221114T221647Z
UID:2522-1676624400-1676905200@www.statscamp.org
SUMMARY:Mediation and Moderation
DESCRIPTION:LIVE STREAM – 4-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview:\nA comprehensive 4-day Stats Camp seminar on Mediation and Moderation. \nSeminar Topics:\n• Classic and contemporary approaches of Mediation and Moderation\n• Estimating mediation effects\n• Estimating Moderation\n• Path analysis\n• Indirect and direct effects\n• Testing intervening variable effects\n• Probing and plotting interactions\n• Combining moderation and mediation \nSeminar Description:\nIn many scientific fields research questions have become more complex. Researchers are no longer simply interested in if one variable (X) is related to another (Y). Instead\, research questions such as: “Why is X related to Y?” and “When is X related to Y?” abound. This course addresses methods to test why two variables are related (mediation) and when two variables are related (moderation). \n\nInstructor: Mwarumba Mwavita\, Ph.D.\n \nMwarumba Mwavita is Director of the Center for Educational Research and Evaluation (CERE) at Oklahoma State University. In addition\, he is an Assistant Professor in the Research\, …Evaluation\, Measurement and Statistics (REMS) program in the School of Educational Studies (SES). \nRead More\n\n\n\nAPA Continuing Education Credits:\n \nThis course offers 16 hours of Continuing Education Credits. Yhat Enterprises\, LLC is approved by the American Psychological Association to sponsor continuing education for psychologists. Yhat Enterprises\, LLC maintains responsibility for this program and its content. \nSeminar Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\n\nLearning Objectives\nLearning Objectives:\nThis comprehensive 3-day statistics training institute on Mediation and Moderation will enable participants to:\n\nEstimate\, test\, and interpret mediated (i.e.\, indirect) effects using PROCESS macro in SPSS\nEstimate\, test\, and interpret moderated (i.e.\, interaction) effects using PROCESS macro in SPSS and other advanced techniques\nCombine mediation and moderation models to test conditional indirect effects.\n\nSeminar Prerequisites\nSeminar Prerequisites:\nRequired: \n\n Basic proficiency in multiple linear regression\nIntermediate proficiency with at least one statistical software package (e.g.\, SPSS\, Stata\, SAS\, R\, etc.).\n\nNot required but advantageous: \n\nAt least limited experience (e.g.\, graduate-level course) path analysis or SEM\n\nInstructor Bio: Mwarumba Mwavita is an Associate Professor in the Research\, Evaluation\, Measurement and Statistics (REMS) and Founding Director of the Center for Educational Research and Evaluation (CERE) at Oklahoma State University. He teaches graduate courses in Multiple Regression\, Multivariate\, Mediation and Moderation \, Multilevel and Program Evaluation and Cost Benefit analysis. His research focus is in the application of GLM methods in investigating education policy\, equity\, and access in addition to STEM education. As the director CERE he plans and conduct program evaluation for funded projects. \nSoftware and Computer Support\nSoftware and Computer Support:\nParticipants need a laptop computer with Wi-Fi and webcam capabilities. \nAll statistical software used at Stats Camp will be available\, free to participants\, on our SMORS (statistical modeling on remote servers) system for the duration of camp.Syllabus\n\n\n\nFriday\nDay 1\n\n\n9:00-9:30\nWelcome and introductions\n\n\n9:30-10:45\nReview of Multiple Regression and Path analysis\n\n\n10:45-11:00\nSnack and refreshments break\n\n\n11:00-12:30\nIntroduction to Mediation\n\n\n12:30-13:30\nLunch break\n\n\n13:30-15:00\nMediation using PROCESS macro with SPSS\n\n\n15:00-15:15\nSnack and refreshments break\n\n\n15:15-17:00\nSerial and Parallel Mediation\n\n\nSaturday\nDay 2\n\n\n9:00-10:45\nComputing\, testing and Interpreting Mediation using PROCESS\n\n\n10:45-11:00\nSnack and refreshments break\n\n\n11:00-12:30\nIntroduction to Moderation\n\n\n12:30-13:30\nLunch break\n\n\n13:30-15:00\nCategorical Moderators Using PROCESS\n\n\n15:00-15:15\nSnack and refreshments break\n\n\n15:15-17:00\nContinuous Moderators and Multiple Moderators\n\n\nSunday \nDay 3\n\n\n9:00-10:45\nGraphing and Interpreting Moderation in PROCESS\n\n\n10:45-11:00\nSnack and refreshments break\n\n\n11:00-12:30\nCombining mediation and moderation\n\n\n12:30-13:30\nLunch break\n\n\n13:30-17:00\nTesting and interpreting conditional Indirect effectsOne-on-one Consultations with instructor\n\n\n\nDownload Sample Slides and Preview Course Video\nPlease fill out and submit the form below to get instant access to sample course materials.
URL:https://www.statscamp.org/courses/mediation-and-moderation/
LOCATION:Livestream and/or Asynchronous:\, If you are unavailable to join live via Zoom\, you can participate asynchronously by viewing the recorded course videos for up to 1 year.
CATEGORIES:Mediation and Moderation,Winter Camp
ATTACH;FMTTYPE=image/jpeg:https://www.statscamp.org/wp-content/uploads/2022/06/mediation-moderation-statistics-course.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20230217T090000
DTEND;TZID=America/Chicago:20230220T150000
DTSTAMP:20221127T103931
CREATED:20220701T031137Z
LAST-MODIFIED:20221114T221605Z
UID:2553-1676624400-1676905200@www.statscamp.org
SUMMARY:Systematic Review and Meta-Analysis
DESCRIPTION:LIVE STREAM – 4-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview:\nAn intermediate 4-day course introducing and practicing the concepts of systematic reviewing and conducting a meta-analysis using R. \nSystematic review and meta-analysis are techniques used to synthesize and summarize large bodies of research literature. Compared to results from a single primary study\, results from a meta-analysis provide greater generalizability\, increased precision\, and the ability to explore heterogeneity across studies. \nYou will use the latest techniques and technical tools to conduct a high-quality systematic review and meta-analysis. You will learn hands-on\, practical\, and applied approaches to conducting reviews by combining lectures with practice material designed to enable you to conduct future meta-analyses. \nSeminar Topics:\n\nUnderstanding the purpose and goals of systematic review and meta-analysis\nConducting a systematic\, comprehensive literature search and screening\nExtracting information from primary studies efficiently and reliably\nCalculating effect sizes and variances\nPerforming a synthesis and moderator analyses using R\nDisseminating the results of a systematic review and meta-analysis\n\nSeminar Description:\nSystematic review and meta-analysis are techniques used to synthesize and summarize large bodies of research literature. Compared to results from a single primary study\, results from a meta-analysis provide greater generalizability\, increased precision\, and the ability to explore heterogeneity across studies (Borenstein\, Hedges\, Higgins\, & Rothstein\, 2010; Pigott\, 2012). Meta-analysis has proven useful for policy makers and practitioners because the findings offer answers to ambiguous questions and synthesize large bodies of literature.\nDuring this camp\, participants will gain all the tools necessary to conduct a high quality systematic review and meta-analysis. Although these procedures can be found in numerous textbooks\, the latest techniques and technical tools often require nuanced and sophisticated applications of numerous online tools and applications. This course will focus on the hands-on\, practical\, and applied approach to conducting reviews by combining lectures with practice material designed to enable participants to conduct future meta-analyses. \n\nInstructor: Joshua R. Polanin\, Ph.D.\n \nSenior Research Scientist at Development Services Group\, Inc.\, a social science research firm that specializes in program evaluation\, systematic reviews\, and technical assistance. Dr. Polanin is the Senior Methodologist on the Department of Education’s What Works Clearinghouse Reviews\, Reporting\, Dissemination\,…and Development (R2D2) contract\, and Senior Statistician for the Substance Abuse and Mental Health Services Administration’s National Registry of Evidence-based Programs and Practices (NREPP). \nRead More\n\n\nAPA Continuing Education Credits:\n \nThis course offers 16 hours of Continuing Education Credits. Yhat Enterprises\, LLC is approved by the American Psychological Association to sponsor continuing education for psychologists. Yhat Enterprises\, LLC maintains responsibility for this program and its content. \nSeminar Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\nLearning Objectives\nLearning Objectives:\nAfter engaging in course lectures and discussions as well as completing the hands-on practice activities with real data\, participants will be able to: \n\nTo understand the concept of and the practical purposes of a systematic review and meta-analysis\nTo learn the practice of systematic review and meta-analysis by conducting hands-on activities\nTo present the findings of a systematic review and meta-analysis to a broad audience\nTo evaluate and critique published systematic reviews and meta-analyses\nTo develop skills to conduct meta-analysis in the statistical software R\n\nParticipants will learn the skills necessary to conduct a small systematic review and meta-analysis as well as the theoretical knowledge required to provide critical assessment of published reviews. \nSeminar Prerequisites\nSeminar Prerequisites:\nThis seminar has no prerequisites. \nSoftware and Computer Support\nSoftware and Computer Support:\nParticipants need a laptop computer with Wi-Fi and webcam capabilities. \nAll statistical software used at Stats Camp will be available\, free to participants\, on our SMORS (statistical modeling on remote servers) system for the duration of camp.Syllabus\n\n\n\nDay 1\n\n\n\n\n9:00 – 10:00\nWelcome and introductions\n\n\n10:00 – 12:30\n1. What is a Systematic Review and Meta-Analysis? And what makes for an effective review?2. Setting up a review for success: Protocol and inclusion/exclusion criteria\n\n\n12:30 – 1:30\nLunch\n\n\n1:30-4:30\n1. Conducting a comprehensive search of the literature2. Practicing a search\n\n\n4:30 – 5:00\nIndividual consultations\n\n\nDay 2\n\n\n\n\n9:00-10:45\n1. Abstract and full-text screening2. Designing a codebook\n\n\n10:45-11:00\nSnack and refreshment break\n\n\n11:00-12:30\n1. Designing a codebook (continued)2. Calculating effect sizes\n\n\n12:30-1:30\nLunch break\n\n\n1:30-3:00\n1. Calculating effect sizes\n\n\n3:00-3:15\nSnack and refreshment break\n\n\n3:15-5:00\n1. Introduction to R2. Synthesizing effect sizes in R\n\n\nDay 3\n\n\n\n\n9:00-10:45\n1. Detecting and explaining heterogeneity\n\n\n10:45-11:00\nSnack and refreshment break\n\n\n11:00-12:30\n1. Publication bias2. Disseminating results\n\n\n12:30-1:30\nLunch break\n\n\n1:30-3:00\n1. Doing a real-world meta-analysis using published results\n\n\n3:00-3:15\nSnack and refreshment break\n\n\n3:15-5:00\n1. Group presentations2. Final thoughts\n\n\n\nDownload Sample Slides and Preview Course Video\nPlease fill out and submit the form below to get instant access to sample course materials.
URL:https://www.statscamp.org/courses/systematic-review-and-meta-analysis/
LOCATION:Livestream and/or Asynchronous:\, If you are unavailable to join live via Zoom\, you can participate asynchronously by viewing the recorded course videos for up to 1 year.
CATEGORIES:Systematic Review and Meta-Analysis,Winter Camp
ATTACH;FMTTYPE=image/jpeg:https://www.statscamp.org/wp-content/uploads/2022/06/systematic-review-meta-analysis-training-course.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20230228T090000
DTEND;TZID=America/Chicago:20230303T150000
DTSTAMP:20221127T103931
CREATED:20221013T214555Z
LAST-MODIFIED:20221114T221510Z
UID:4590-1677574800-1677855600@www.statscamp.org
SUMMARY:Item Response Theory Models in R
DESCRIPTION:LIVE STREAM – 4-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview: Item Response Theory Course\nThis interactive training course will introduce the concepts of unidimensional and multidimensional IRT models and provide instruction\, demonstration\, and hands-on opportunities of using the free R software to estimate commonly used IRT models. \nSeminar Topics:\n\nThe Rasch\, 1PL\, 2PL\, and 3PL IRT models will be discussed for unidimensional dichotomous data.\nThe GR and GPC models will be presented for polytomous data.\nThe 2PL model will be explained for multidimensional dichotomous data.\nThe analysis of these models and types of data will be implemented using the eRm\, ltm\, and mirt packages in R.\n\nSeminar Description:\nThis item response theory course is perfect for anyone seeking to enhance their researcher skills of using R to do IRT analysis and advance their knowledge of IRT. These may be students\, faculty\, and researchers from a variety of fields that utilize IRT (e.g.\, education\, psychometrics\, psychology\, and testing). The audience is expected to have a basic to intermediate knowledge level of general statistics. No prior knowledge of experience of using R to do IRT is necessary. The instructions and training on IRT and the use of R will be taught at an introductory to intermediate level. \n\nInstructor: Ki Cole\, Ph.D.\n \nKi Cole\, Ph.D. is an Associate Professor for Research\, Evaluation\, Measurement and Statistics (REMS) in the College of Education and Human Sciences (CEHS). She is an Oklahoma native and attended the land-grant University of Arkansas (BS\, MS\, and PhD). She joined the REMS faculty at OSU in August 2014 and has served as the Course Coordinator for the REMS service courses since 2019. Dr. Cole is an active participant in all areas of teaching\, research\, and service. Her primary areas of study are in the design\,… evaluation\, and use of tests and surveys. She is the recipient of the 2020 Marguerite Scrubbs Award for Meritorious Early Career Research and 2018 Distinguished Faculty Research Award in CEHS. She teaches graduate courses (e.g.\, Statistical Methods\, ANOVA\, Factor Analysis\, and Item Response Theory) and serves on graduate student committees across colleges at OSU. She has co-authored one textbook and publishes in Educational and Psychological Measurement\, International Journal of Testing\, and Journal of Quantitative Research in Education. She serves as External Evaluator for various grants and has served as a reviewer of grant proposals for the National Science Foundation (NSF). Ki has served as the CEHS representative to Faculty Council since 2019. \nRead More\n\n\nAPA Continuing Education Credits:\n \nThis course offers ? hours of Continuing Education Credits. Yhat Enterprises\, LLC is approved by the American Psychological Association to sponsor continuing education for psychologists. Yhat Enterprises\, LLC maintains responsibility for this program and its content. \nSeminar Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\nLearning Objectives\nLearning Objectives:\nThe first objective of this item response theory course is to introduce participants to commonly used IRT models. Evidence of meeting this objective will be the following: \n\nParticipants will be able to distinguish between the Rasch\, one-parameter (1PL)\, two-parameter (2PL)\, three-parameter (3PL)\, graded response (GR) and generalized partial credit (GPC) models.\nParticipants will be able to determine which unidimensional model to use when data are of a particular structure: dichotomous or polytomous.\nParticipants will be able to apply a multidimensional model to simple and complex data.\nParticipants will have an understanding of item parameters (e.g. difficulty\, discrimination\, threshold\, etc.)\, and how they relate to the item (or category) characteristic curves for each IRT model.\nParticipants will have a general understanding of item calibration and latent ability estimation processes.\nParticipants will know the desired conditions for proper calibrations of each IRT model.\n\nThe second objective of this item response theory course is to provide detailed instructions on how to use the IRT-related packages (i.e.\, eRm\, ltm\, and mirt) in R for IRT analysis. Evidence of meeting this objective will be the following: \n\nParticipants will be able to determine which package to use based on the model choice and the data structure.\nParticipants will be able to upload a dataset into R.\nParticipants will be able to call a needed function and supply the appropriate parameters to analyze a dataset and produce test and item characteristic and information plots.\nParticipants will be able to interpret the output of a called function\, including the item parameters and ability estimates\, and its added components.\n\nSeminar Prerequisites\nSeminar Prerequisites:\nPrior knowledge of R software is not required. A basic understanding of unidimensional IRT models is highly recommended. Familiarity with writing syntax may also be helpful for using R but is not essential. The course will be taught under the assumptions that participants have an elementary-level of knowledge about IRT and have little to no experience using R. \nIf this course is not a good fit for you please view our complete statistics training course list. \nSoftware and Computer Support\nSoftware and Computer Support:\nParticipants need a laptop computer with Wi-Fi and webcam capabilities. \nThe R software will be used for all analyses\, and is freely available for download. \nAll statistical software used at Stats Camp will be available\, free to participants\, on our SMORS (statistical modeling on remote servers) system for the duration of camp.Syllabus\n\n\n\n\nDay 1\n\n\nC.S.T.\n\n\nTime\n\n\nTopic\n\n\n\n\n9:00-9:30\n\n\n11:00 – 11:30\n\n\n30 min\n\n\nWelcome and introduction\nCheck R Installations on Computers\n\n\n\n\n9:30-10:45\n\n\n\n\n\n1hr 15 min\n\n\nUnidimensional Rasch and 1PL models\n\n\n\n\n10:45-11:00\n\n\n12:45 – 1:00\n\n\n15 min\n\n\nSnack and Refreshment Break\n\n\n\n\n11:00-12:30\n\n\n\n\n\n1hr 30 min\n\n\nApplications of Rasch and 1PL models in R\n\n\n\n\n12:30-1:30\n\n\n2:30 – 3:30\n\n\n1 hr\n\n\nLunch Break\n\n\n\n\n1:30-3:00\n\n\n\n\n\n1hr 30 min\n\n\nUnidimensional 2PL and 3PL models\n\n\n\n\n3:00-3:15\n\n\n5:00 – 5:15\n\n\n15 min\n\n\nSnack and Refreshment Break\n\n\n\n\n3:15-4:45\n\n\n\n\n\n1hr 30 min\n\n\nApplications of 2PL and 3PL models in R\n\n\n\n\n4:45-5:00\n\n\n\n\n\n15 min\n\n\nDe-brief\n\n\n\n\nDay 2\n\n\n\n\n\n\n\n\n\n\n\n\n\n9:00-9:15\n\n\n11:00 – 11:15\n\n\n15 min\n\n\nReview\n\n\n\n\n9:15-10:45\n\n\n\n\n\n1hr 30 min\n\n\nUnidimensional GR model\n\n\n\n\n10:45-11:00\n\n\n12:45 – 1:00\n\n\n15 min\n\n\nSnack and Refreshment Break\n\n\n\n\n11:00-12:30\n\n\n\n\n\n1hr 30 min\n\n\nApplications of GR model in R\n\n\n\n\n12:30-1:30\n\n\n2:30 – 3:30\n\n\n1 hr\n\n\nLunch Break\n\n\n\n\n1:30-3:00\n\n\n\n\n\n1hr 30 min\n\n\nUnidimensional GPC model and Application in R\n\n\n\n\n3:00-3:15\n\n\n5:00 – 5:15\n\n\n15 min\n\n\nSnack and Refreshment Break\n\n\n\n\n3:15-4:45\n\n\n\n\n\n1hr 30 min\n\n\nOpen Discussion and Consulting\n\n\n\n\n4:45-5:00\n\n\n\n\n\n15 min\n\n\nDe-brief\n\n\n\n\nDay 3\n\n\n\n\n\n\n\n\n\n\n\n\n\n9:00-9:15\n\n\n11:00 – 11:30\n\n\n15 min\n\n\nReview\n\n\n\n\n9:15-10:45\n\n\n\n\n\n1hr 30 min\n\n\nMultidimensional 2PL model for Simple Structure Data\n\n\n\n\n10:45-11:00\n\n\n12:45 – 1:00\n\n\n15 min\n\n\nSnack and Refreshment Break\n\n\n\n\n11:00-12:30\n\n\n\n\n\n1hr 30 min\n\n\nApplication of MIRT 2PL for Simple Structure Data\n\n\n\n\n12:30-1:30\n\n\n2:30 – 3:30\n\n\n1 hr\n\n\nLunch Break\n\n\n\n\n1:30-3:00\n\n\n\n\n\n1hr 30 min\n\n\nMIRT for Complex Structure and Bifactor Models\n\n\n\n\n3:00-3:15\n\n\n5:00 – 5:15\n\n\n15 min\n\n\nSnack and Refreshment Break\n\n\n\n\n3:15-4:45\n\n\n\n\n\n1hr 30 min\n\n\nOpen Discussion and Consulting\n\n\n\n\n4:45-5:00\n\n\n\n\n\n15 min\n\n\nDe-brief\n\n\n\n\nDay 4\n\n\n\n\n\n\n\n\n\n\n\n\n\n9:00-9:15\n\n\n11:00 – 11:30\n\n\n15 min\n\n\nReview\n\n\n\n\n9:15-10:45\n\n\n\n\n\n1hr 30 min\n\n\nOpen Discussion and Consulting\n\n\n\n\n10:45-11:00\n\n\n12:45 – 1:00\n\n\n15 min\n\n\nReview\n\n\n\n\n11:00-12:30\n\n\n\n\n\n1hr 30 min\n\n\nOpen Discussion and Consulting\n\n\n\n\n12:30-1:30\n\n\n2:30 – 3:30\n\n\n1 hr\n\n\nDe-brief\n\n\n\n\nDownload Sample Slides and Preview Course Video\nPlease fill out and submit the form below to get instant access to sample course materials.
URL:https://www.statscamp.org/courses/item-response-theory-models-in-r-studio/
LOCATION:Livestream and/or Asynchronous:\, If you are unavailable to join live via Zoom\, you can participate asynchronously by viewing the recorded course videos for up to 1 year.
CATEGORIES:Item Response Theory
ATTACH;FMTTYPE=image/jpeg:https://www.statscamp.org/wp-content/uploads/2022/10/item-response-theory.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20230316T090000
DTEND;TZID=America/Chicago:20230319T150000
DTSTAMP:20221127T103931
CREATED:20220816T020031Z
LAST-MODIFIED:20221014T033645Z
UID:4242-1678957200-1679238000@www.statscamp.org
SUMMARY:Network Psychometrics
DESCRIPTION:LIVE STREAM – 4-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview:\nAn introductory 4-day course in the application of psychometrics. Participants should be proficient specific to the material covered in a two-semester graduate-level social science statistics course sequence. \nSeminar Topics:\n\nMeasurement and statistical concepts specific to psychometrics\nScaling\, scaling models & scale development – stimulus\, response and subject centered\nValidity – conceptual and statistical aspects necessary for evidential arguments\nIntroduction to Factor analysis – traditional\, IRT and SEM-based approaches/connections\nReliability – classical and modern approaches to estimation of score reliability\nIntroduction to Item Response Theory\n\nSeminar Description:\nPsychometrics is defined as the science of evaluating the characteristics of tests or other devices designed to measure psychological attributes of people. Tests are broadly defined as devices for measuring ability\, aptitude\, achievement\, attitudes\, interests\, personality\, cognitive functioning\, and mental health. Application of psychometrics to psychology and social/behavioral science constitutes an organized effort to (a) properly use theory-based measurement procedures for the development of tests and other measurement instruments for inter- and intraindividual research designs and (b) incorporate current best practices for applying measurement theory\, item/scale development\, reliability estimation (classical and modern)\, factor analysis/IRT and establishing statistical evidence of score validity through a unified approach. advance knowledge in psychological and sensory processes. Participants will receive an electronic copy of all course materials\, including lecture slides\, practice datasets\, software scripts\, relevant supporting documentation\, and recommended readings. Participants will also have access to a video recording of the course. \n\nInstructor: Larry Price\, Ph.D.\n \nLarry Price\, Ph.d. PStat is the Director of the Data Analytics & Methodology at Texas State University. This university-wide role involves supervising a team of quantitative methodologists in conceptualizing and writing the analytic segments of large-scale competitive grant proposals for funding agencies such as the Department of Education/Institute of Education Sciences\, National Science Foundation\, National Institutes of Health\, National Institute on Standards\, and the Department of Defense in collaboration with interdisciplinary research teams. His research has been funded by agencies such as the Department of Education/Institute of Education Sciences\, National Science Foundation\, National Institutes of Health\, National Institute on Standards\, and the Department of Defense. \nRead More\n\n\nAPA Continuing Education Credits:\n \nThis course offers ? hours of Continuing Education Credits. Yhat Enterprises\, LLC is approved by the American Psychological Association to sponsor continuing education for psychologists. Yhat Enterprises\, LLC maintains responsibility for this program and its content. \nSeminar Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\nLearning Objectives\nLearning Objectives:\n\nAcquire a basic understanding of the role of psychometrics as applied to social and behavioral sciences.\nDevelop a clear understanding of the conceptual and theoretical basis of measurement and statistical concepts specific to psychometrics.\nAcquire knowledge of how to properly apply psychometric techniques such as scale development\, item analysis/refinement\, score reliability and statistical validity.\nGain knowledge of how to apply factor analysis using traditional and structural equation modeling approaches.\nGain knowledge of how to apply generalizability theory for estimating variance components and score reliability when classical test theory model is inadequate.\nAcquire basic knowledge of how and why to apply item response theory for scaling test data.\n\nSeminar Prerequisites\nSeminar Prerequisites:\nRequired: \n\nIntermediate proficiency in basic statistical theory as would be gained in a 1st year graduate course.\n\nNot required but advantageous: \n\nLimited experience (e.g.\, graduate-level course) with classical measurement theory and concepts.\n\nNo level of proficiency beyond basic awareness is assumed for skills related to: \n\nAdvanced mathematical or statistical topics such as matrix algebra.\n\nSoftware and Computer Support\nSoftware and Computer Support:\nParticipants need to bring a laptop computer with Wi-Fi capabilities. Students should have access to IBM SPSS\, version 21.0 or higher and Mplus\, version 7.1 or higher and R. \nAll statistical software used at Stats Camp will be available\, free to participants\, on our SMORS (statistical modeling on remote servers) system for the duration of camp.Syllabus\n\n\n\nDay 1\n\n\n\n\n9:00-9:30\nWelcome and introductions\n\n\n9:30-10:30\nMeasurement & statistical concepts\n\n\n10:30-10:45\nSnack and refreshment break\n\n\n10:45-12:30\nScaling and scaling models – achievement\, ability\, attitude & perception\n\n\n12:30-1:30\nLunch break\n\n\n1:30-3:00\nTechniques for item and test development\, evaluation & refinement\n\n\n3:00-3:15\nSnack and refreshment break\n\n\n3:15-5:00\nValidity – criterion\, content & construct considerations\n\n\nDay 2\n\n\n\n\n9:00-10:45\nStatistical aspects of the score validation process\n\n\n10:45-11:00\nSnack and refreshment break\n\n\n11:00-12:30\nFactor analysis – foundations\, types and estimating factor models using exploratory and confirmatory approaches – part 1\n\n\n12:30-1:30\nLunch break\n\n\n1:30-3:00\nFactor analysis – a unified model for test theory and application\, estimating factor models using structural equation modeling – part 2\n\n\n3:00-3:15\nSnack and refreshment break\n\n\n3:15-5:00\nIndividual Consultations (alternatively start reliability presentation)\n\n\nDay 3\n\n\n\n\n9:00-10:45\nReliability of test scores – foundations/application of classical test theory; Using/applying structural equation modeling and IRT for score reliability estimation\n\n\n10:45-11:00\nSnack and refreshment break\n\n\n11:00-12:30\nIntroduction to Item Response Theory – theory and applications for applied psychometrics; Relationship to structural equation modeling\n\n\n12:30-1:30\nLunch break\n\n\n1:30-3:00\nIntroduction to generalizability theory\n\n\n3:00-3:15\nSnack and refreshment break\n\n\n3:15-5:00\nIndividual Consultations (or continue presentation of material)\n\n\n\n \nDownload Sample Slides and Preview Course Video\nPlease fill out and submit the form below to get instant access to sample course materials.
URL:https://www.statscamp.org/courses/network-psychometrics-training/
LOCATION:Livestream and/or Asynchronous:\, If you are unavailable to join live via Zoom\, you can participate asynchronously by viewing the recorded course videos for up to 1 year.
CATEGORIES:Network Psychometrics
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BEGIN:VEVENT
DTSTART;TZID=Africa/Johannesburg:20230414T080000
DTEND;TZID=Africa/Johannesburg:20230417T170000
DTSTAMP:20221127T103931
CREATED:20220707T010405Z
LAST-MODIFIED:20220921T033501Z
UID:3103-1681459200-1681750800@www.statscamp.org
SUMMARY:Analysis Retreat: South Africa
DESCRIPTION:All-Inclusive In-Person – 4-day Statistics Training Retreat\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nRetreat Overview:\n4 days of intensive analysis\, software solutions\, best practices and options that brings your research up to the world-class level needed to get published in a top-tier journal. Analysis Retreat will make your research “submission ready” with the guided assistance of our team of experts.\nStats Camp Analysis Retreat is a unique and powerful 4 day opportunity to have a highly cited International team of experts vet your research and increase the likelihood of peer review approval and publishing. Our team puts the advantage squarely in your corner with four days of intensive analysis\, software solutions\, best practices and options that brings your research up to the world-class level needed to get published in a top-tier journal. Analysis Retreat will make your research “submission ready.” Build solid confidence for tenure and promotion. Stats Camp Analysis Retreat is the positive action you can take NOW to get your career on the fast track. Due to the intense personal focus of the retreat\, seats are very limited and fill fast. \nAnalysis Retreat Includes:\n\n\nIn person one-on-one training\nHotel lodging provided\n\nAll meals and daily beverages & snacks provided\n\nMaterials\, downloads\, and software access\n\n\n\n\n\nRetreat Description\nRetreat Description:\n\nTodd D. Little\, Ph.D. developed Stats Camps Analysis Retreats to address the need for specialized expertise that is often needed to move complex research projects to approval and publication. Dr. Little is a preeminent award-earning scholar who has published over 300 peer-reviewed works that have garnered 46\,611+ citations. \nMembers of the Stats Camp team are highly experienced and many have decades of experience in resolving complex analytical challenges with an understanding of best-practices procedures. You will have exclusive access to their expertise as they guide you to a final publication-ready product. \nExpert consulting at this level can quickly run into the tens of thousands of dollars. By bringing our team together with you at an exclusive and personalized all-inclusive Stats Camp Analysis Retreat\, we can offer this unprecedented access at a much more affordable price. \n\nWho Should Attend?\nWho Should Attend?\nIf you are in the pre-data analysis phase\, Stats Camp staff will engage with you to develop a testable research hypothesis\, a fully articulated analysis plan\, and ensure full understanding of the analysis model\, the nature of its results\, and their implications. \nIf you are in the analysis phase\, Stats Camp staff will work with you to learn the software syntax needed to analyze your data\, interpret the output and understand the implications of your findings. \nBreakout sessions will cover a vast array of techniques and topics. Some topics are predetermined while others will be prepared specifically to meet your needs and the needs of the other participants (a survey will be given prior to the event to develop this need list of topics to cover). \nWhat to expect\nWhat to expect:\nParticipants in our past annual Stats Camp Analysis Retreats were overwhelming positive about the individualized learning that took place as well as the opportunity to work with their own data – a practical aspect that makes learning easier. Because participants are enabled to ask questions and get immediate feedback\, learning the practical analytic skills is facilitated. Also\, participants learned about different topics tailored to their learning needs. \nParticipants uniformly exclaimed that the learning activities and interaction with the Stats Camp instructors contributed to publishing more articles and increased the quality of their research\, which has allowed them to target higher impact factor journals. Because quantitative methods are a critical gap in most fields\, the Stats Camp experience helped clarify the essential importance of quantitative methods. By learning the basics of applying these methods\, the knowledge is thereby carried over to other colleagues and students\, and ultimately helping to close the gap. \nThe Stats Camp Analysis Retreat is a perfect combination of learning and fun from the comfort of a beautiful lodge in an amazing destination\, networking in a collegial and social context. Having the stats camp in a retreat format\, gives you ample time\, motivation\, and access to incomparable expertise to propel your research forward. \nSchedule\n\nWelcome (Friday) – April 17 \nWe start with a short lecture on regression versus SEM modeling\, when to use which method\, and explain the break-out sessions. \n10:00 – Welcome and overview program \n10:30 – Key-note lecture 1 (Todd Little & Rens van de Schoot) \nAfternoon break-out sessions in two groups based on whether there is a clear analysis plan. Group 1: already working on analysis. Group 2: no data yet. For both groups\, the goal is to answer questions like: \n\nWhat exactly is your research question?\nWhat are your hypotheses?\nAre your analyses exploratory or confirmatory?\nWhat variables do you have and what is their measurement level?\nMake a drawing of your model (observed/latent).\n\n\nDay 1 (Friday) – April 14\n \nContinuation of Monday’s break-out sessions. \nMorning breaks will be from 10:00 – 10:50. \nLunch will be from 12:30 – 13:30. \nAfternoon break-out sessions with mini-lectures (conversations) on statistical models: \n\nBasic multivariate statistics (T-test\, anova\, etc.)\nBasic SEM-CFA\nMultilevel analyses\nLongitudinal analyses\nBayesian analyses\n\nDuring the day\, we will also have individual/group consultation appointments to answer specific questions from participants. \nAfternoon breaks will be from 15:00 – 15:15. \nSessions will end at 16:30. \n\n\n\n\n\nDay 2 (Saturday) – April 15\n \nMorning Study and Consultations \nGeneral meeting to explain break-out sessions (15 mins) \nMorning break-out sessions introducing software: \n\nIntro lecture Mplus with simple regression and CFA\nIntro lecture R + Rstudio\, how to upload data\, run simple regression using lm function\nAdvanced Mplus with LGM\, LCA\, LGMM\nAdvanced R with CFA and LGM in lavaan\n\n10:00 – 10:50: Morning Break. \n12:00 – 12:30: General meeting discussing results. \n12:30 – 13:30: Lunch \nAfternoon break-out sessions working on your own data (or work on exercises if you do not have data yet) based on level of experience \n\nBasic Mplus\nBasic R\nAdvanced Mplus or R\nParticipant nominated topics\n\nWe will also have individual/small group consultation appointments \n15:00 – 15:15: Afternoon Break \n16:30: Sessions end. \n\n\n\n\n\nDay 3 (Sunday) – April 16\n \nGeneral meeting to explain break-out sessions (15 mins) \nMorning break-out sessions based on level of experience \n\nBasic Mplus\nBasic R\nAdvanced Mplus or R\nParticipant nominated topics\nIndividual/small group consultation appointments\n\n10:00 – 10:50: Morning break. \n12:00 – 12:30: General meeting discussing results. \n12:30 – 13:30: Lunch \n13:30 – 14:45: Key-note lecture 2 (Todd Little & Rens van de Schoot) \n\n\n\n\n\n\nDay 4 (Monday) – April 17\n \nContinue to work on own data and get individual Zoom feedback on taking the next steps. \n11:00 – 11:30: Closing Zoom meeting \n11:30 – Afternoon: Open consultation sessions \n4:00 Retreat close \n\n\n\n\nDownload Sample Slides and Preview Course Video\nPlease fill out and submit the form below to get instant access to sample course materials.
URL:https://www.statscamp.org/courses/analysis-retreat-south-africa/
LOCATION:Mongena Lodge – South Africa\, Dinokeng Game Reserve\, Rust de Winter Road\, Hammanskraal\, 0400\, South Africa
CATEGORIES:Analysis Retreat
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BEGIN:VEVENT
DTSTART;TZID=America/Denver:20230605T090000
DTEND;TZID=America/Denver:20230609T170000
DTSTAMP:20221127T103931
CREATED:20220701T034911Z
LAST-MODIFIED:20221114T221306Z
UID:2570-1685955600-1686330000@www.statscamp.org
SUMMARY:Bayesian Data Analysis
DESCRIPTION:IN PERSON – 5-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview:\nThis is a comprehensive five day Seminar on Bayesian Data Analysis. This is our most popular training event each year where students and professionals have the opportunity to learn directly from a vetted statistics instructor. The live lectures and one-on-one Q & A provide a rare chance to bring your own personal research and let us help you break through any roadblocks standing in the way of progressing your work. The intended audience is advanced students\, faculty\, and other researchers\, from all disciplines\, who want a ground-floor introduction to doing Bayesian data analysis.\n \nSeminar Topics:\n\nThe rich information provided by Bayesian analysis and how it differs from traditional (Frequentist) statistical analysis\nThe concepts of Bayesian reasoning along with the easy math and intuitions for Bayes’ rule\nThe concepts and hands-on use of modern algorithms (“Markov chain Monte Carlo”) that achieve Bayesian analysis for realistic applications\nHow to use the free software R for Bayesian analysis.\nAn extensive array of applications\, including comparison of two groups\, ANOVA-like designs\, linear regression\, logistic regression\, multilevel regression\, and growth models\, count regression\, robust regression\, regularization and polynomials\, and missing data treatments in Bayesian inference.\n\nSeminar Description:\nMany fields of science are transitioning from null hypothesis significance testing (NHST) to Bayesian data analysis. Bayesian analysis provides rich information about the relative credibilities of all candidate parameter values for any descriptive model of the data\, without reference to p values. Bayesian analysis applies flexibly and seamlessly to complex hierarchical models and realistic data structures\, including small samples\, large samples\, unbalanced designs\, missing data\, censored data\, outliers\, etc. Bayesian analysis software is flexible and can be used for a wide variety of data-analytic models. This seminar shows you how to do Bayesian data analysis\, hands on. \n\nInstructor: Mauricio Garnier-Villarreal\, Ph.D.\n \nMauricio is a full time assistant professor at Vrije Universiteit Amsterdam. His Ph.D focused on Quantitative Psychology at the University of Kansas completed in the summer of 2016. His research focus is on the application of Bayesian methods to complex structure data for longitudinal analysis\, from both mixed-effects and SEM models. He has experience not only working in the test and development of methods\, but also in the application of these in data; in different fields like special education\, cognitive decline in aging\, healthy aging… (orcid.org/0000-0002-2951-6647). He has been involved in the Stats Camp since 2011. \nRead More\n\n\n\nInstructor: Esteban Montenegro\, Ph.D.\n \nI’m a researcher at the UC Davis Alzheimer’s Disease Center- East Bay. I conduct data analysis using advanced statistical methods such as latent variable modeling. I have 6 years of experience programming in R\, and I love learning about Linux and statistical new tools.…I’m always open to new projects and ideas\, I collaborate with several teams around the world on topics related to healthy aging\, Alzheimer Disease and other health related topics. \nRead More\n\n\nAPA Continuing Education Credits:\n \nThis course offers 29 hours of Continuing Education Credits. Stats Camp Foundation is approved by the American Psychological Association to sponsor continuing education for psychologists. Stats Camp Foundation maintains responsibility for this program and its content. \nSeminar Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\n\nLearning Objectives\nLearning Objectives:\nAfter engaging in course lectures and discussions as well as completing the hands-on practice activities with real data\, participants will be able to:\n\nDescribe the fundamental properties of Bayesian reasoning.\nImplement Bayes’ rule\nUtilize Markov Chain Monte Carlo in the brms package for R to evaluate Bayesian models.\nApply the appropriate prior distribution to a Bayesian analysis.\nConduct generalized linear models using Bayesian estimation\, including simple linear regression\, multiple regression\, and robust regression.\nConduct a T-test using Bayesian estimation.\nConduct a Bayesian ANOVA.\nEvaluate Logistic and Count regression models using Bayesian estimation.\nEvaluate Multilevel linear regression models using Bayesian estimation.\nConduct a Bayesian Multilevel growth curve analysis.\nUnderstand how issues of missing data are addressed in the Bayesian framework.\nExplain the difference between frequentist and Bayesian statistics\nCritically evaluate applications of Bayesian analysis in scientific studies.\nAnalyze data using Bayesian techniques in R.\nSpecify\, estimate\, evaluate\, and compare different Bayesian models to fit possible hypothesis.\n\nParticipants are encouraged to bring data of interest to work with during the week\, there will be time to work on it with help of the instructor. Work on datasets of interest will magnify the learning and impact of the course. \nParticipants will also complete the course with a foundation for future learning about Bayesian statistics and knowledge about available resources to guide such endeavors. \nSeminar Prerequisites\nSeminar Prerequisites:\nRequired: \n\nBasic proficiency in multiple linear regression\, the generalized linear model.\nIntermediate proficiency with at least one statistical software package (e.g.\, SPSS\, Stata\, SAS\, R\, etc.).\n\nNot required but advantageous: \n\nAt least limited experience (e.g.\, graduate-level course) with multilevel models\nIntermediate proficiency in R\, or syntax base software\n\nNo level of proficiency beyond basic awareness is assumed for skills related to: \n\nBayesian Statistics\nData analysis using R\n\nSoftware and Computer Support\nSoftware and Computer Support:\nIt is important to bring a notebook computer to the seminar\, so you can run the programs and see how their output corresponds with the presentation material. Please install the software before arriving at the seminar. We’ll be estimating the examples in R language using the packages rstan and brms. The latter will be the main package in this course. You can read more about brms package: https://mc-stan.org/users/interfaces/brms. \nSeminar Audience\nSeminar Audience:\nThe intended audience is advanced students\, faculty\, and other researchers\, from all disciplines\, who want a ground-floor introduction to doing Bayesian data analysis. \nSeminar Files\nSeminar Files\nBelow are links to seminar files for those who enrolled in the seminar. Please download these files onto your computer before the first day of the seminar. The files are password protected to respect the intellectual property rights of the instructors. By using your login information you agree not to share your login information or the content protected by it. \nInstructor will provide password on first day of seminar:\nClick Here to Access Bayesian Data Analysis Seminar Files \nAll statistical software used at Stats Camp will be available\, free to participants\, on our SMORS (statistical modeling on remote servers) system for the duration of camp.Syllabus\n\n\n\nSummer Stats Camp 2023: Bayesian Data Analysis\n\n\nMonday\nJune 5\, 2023\n\n\n9:00–10:45\nBayesian reasoning generally\n\n\n10:45–11:00\nRest Break\n\n\n11:00–12:30\nPerfidious p values and the con game of confidence intervals\n\n\n12:30–1:30\nRest Break\n\n\n1:30–3:15\nBayes’ rule\, grid approximation\, and R; Simple examples to train intuition\n\n\n3:15–3:30\nRest Break\n\n\n3:30–5:00\nMarkov Chain Monte Carlo\n\n\nTuesday\nJune 6\, 2023\n\n\n9:00–10:45\nBrief introduction to R and Rstudio\n\n\n10:45–11:00\nRest Break\n\n\n11:00–12:30\nKnow your distributions. Application of priors\n\n\n12:30–1:30\nRest Break\n\n\n1:30–3:15\nGeneral linear model. Multiple regression\, and robust regression\n\n\n3:15–3:30\nRest Break\n\n\n3:30–5:00\nGeneral linear model. Robust regression\n\n\nWednesday\nJune 7\, 2023\n\n\n9:00–10:45\nModel comparison\n\n\n10:45–11:00\nRest Break\n\n\n11:00–12:30\nGeneral linear model. T-test and ANOVA\n\n\n12:30–1:30\nRest Break\n\n\n1:30–3:15\nRegularization and polynomials in linear regression\n\n\n3:15–3:30\nRest Break\n\n\n3:30–5:00\nGeneralized linear Model. Logistic regression\n\n\nThursday\nJune 8\, 2023\n\n\n9:00–10:45\nGeneralized linear Model: Count regression\n\n\n10:45–11:00\nRest Break\n\n\n11:00–12:30\nMultilevel linear regression\n\n\n12:30–1:30\nRest Break\n\n\n1:30–3:15\nMultilevel linear regression\n\n\n3:15–3:30\nRest Break\n\n\n3:30–5:00\nMultilevel growth curve/repeated measures\n\n\nFriday\nJune 9\, 2023\n\n\n9:00–10:45\nIntroduction to missing data\n\n\n10:45–11:00\nRest Break\n\n\n11:00–12:30\nMissing data in Bayesian inference\n\n\n12:30–1:30\nRest Break\n\n\n1:30~3:30\nIndividual Consultations\n\n\n\nDownload Sample Slides and Preview Course Video\nPlease fill out and submit the form below to get instant access to sample course materials.
URL:https://www.statscamp.org/courses/bayesian-data-analysis/
LOCATION:Embassy Suites – Albuquerque\, 1000 Woodward Place NE\, Albuquerque\, New Mexico\, 87102
CATEGORIES:Bayesian Data Analysis Seminar,Summer Camp
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BEGIN:VEVENT
DTSTART;TZID=America/Denver:20230605T090000
DTEND;TZID=America/Denver:20230609T170000
DTSTAMP:20221127T103931
CREATED:20220701T041915Z
LAST-MODIFIED:20221114T221220Z
UID:2581-1685955600-1686330000@www.statscamp.org
SUMMARY:Introduction to Social Network Analysis using R and Rsiena
DESCRIPTION:IN PERSON – 5-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview:\nMorning sessions will consist primarily of lectures and class discussions. Afternoon sessions will be dedicated to lab demonstrations and activities designed to help participants become users of social network methods and software. Participants are not required but encouraged to bring their own complete network data. The Friday afternoon session will be reserved for consulting on individual projects. \nSurvey a variety of approaches to collecting and analyzing network data at single and multiple points in time using R software. Topics include basic statistics and visualization\, network regression/QAP\, exponential random graph models (ERGMs)\, and stochastic actor-oriented models (RSiena). The workshop will consist of a mixture of classroom teaching and hands-on computer work. As such\, this is a great introduction to R for anyone! Network data will be provided for the lab activities and participants will conduct some type of analysis every day. This is an applied seminar that will take you from novice to proficient in 5 days! \nSeminar Topics:\n\nComing Soon…\n\nSeminar Description:\nThis summer institute is designed primarily for researchers who are interested in conducting social network research\, particularly those who are embarking upon it for the first time. \n\nInstructor: Leslie Echols\, Ph.D.\n \nLeslie is an Assistant Professor in the Department of Psychology at Missouri State University. She holds a Ph.D. in education with a specialization in human development and psychology from University of California\, Los Angeles. She also hold a M.S. in education from City University of New York. She is a former elementary education teacher and currently studies peer relations in the school context. Specifically\,… much of her research investigates the role of school ethnic composition and scheduling practices on friendship and victimization among classmates and other peers. \nRead More\n\n\n\nInstructor: Michael D. Siciliano\, Ph.D.\n \nMichael is an Associate Professor in the Department of Public Administration at the University of Illinois at Chicago (UIC). He holds a Ph.D. in public policy and public administration from the University of Pittsburgh and a master’s in public policy analysis from Carnegie Mellon University. Michael’s work investigates the factors influencing network formation as well as the effect of social structure on individual and collective behavior\, decision-making\, and performance. … Michael has taught network analysis training seminars and workshops for the Public Management Research Association\, the Midwest Association for Public Opinion Research (MAPOR)\, the Science of Team Science Conference\, the Center for Disaster Management at the University of Pittsburgh\, and the Center for Clinical and Translational Science at UIC. \nRead More\n\n\nAPA Continuing Education Credits:\n \nThis course offers 23 hours of Continuing Education Credits. Stats Camp Foundation is approved by the American Psychological Association to sponsor continuing education for psychologists. Stats Camp Foundation maintains responsibility for this program and its content. \nSeminar Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\n\nLearning Objectives\nLearning Objectives:\nAfter engaging in course lectures and discussions as well as completing the hands-on practice activities with real data\, participants will be able to:\n\nIdentify the differences between traditional data and social network data.\nConduct social network analysis in the R environment.\nDescribe the relationship between the individual and social network characteristics.\nGenerate visualizations of social networks using R.\nPractice working with matrices\, network objects\, and basic linear models.\nDiscuss Exponential Random Graph (ERG) models in the context of cross-sectional data.\nBuild ERG models in R.\nInterpret results and parameters from ERG models.\nDemonstrate how to interpret an ERG model diagnostics.\nDiscuss SIENA models in the context of longitudinal data.\nSelect proper SIENA model.\nInterpret SIENA model parameters.\nEvaluate SIENA model fit.\nTroubleshoot SIENA models.\nIdentify advanced applications of in social network analysis.\nApply social analysis techniques to personal research questions.\n\nSeminar Prerequisites\nSeminar Prerequisites:\nRequired: \n\nBasic proficiency in multiple linear regression\, the generalized linear model.\nIntermediate proficiency with at least one statistical software package (e.g.\, SPSS\, Stata\, SAS\, R\, etc.).\n\nNot required but advantageous: \n\nAt least limited experience (e.g.\, graduate-level course) with multilevel models\nIntermediate proficiency in R\, or syntax base software\n\nNo level of proficiency beyond basic awareness is assumed for skills related to: \n\nBayesian Statistics\nData analysis using R\n\nSoftware and Computer Support\nSoftware and Computer Support:\nComing Soon… \nSeminar Audience\nSeminar Audience:\nThis summer institute is designed primarily for researchers who are interested in conducting social network research\, particularly those who are embarking upon it for the first time. The seminar will provide information on data collection and visualization\, and will focus on the use of exponential random graph models (ERGMs; cross-sectional network analysis) and stochastic actor-oriented models (Siena\, longitudinal network analysis) with in the R programming environment. R novices are welcome! \nSeminar Files\nSeminar Files\nBelow are links to seminar files for those who enrolled in the seminar. Please download these files onto your computer before the first day of the seminar. The files are password protected to respect the intellectual property rights of the instructors. By using your login information you agree not to share your login information or the content protected by it. \nInstructor will provide password on first day of seminar:\nClick Here to Access Introduction to Social Network Analysis using R and RSiena \nAll statistical software used at Stats Camp will be available\, free to participants\, on our SMORS (statistical modeling on remote servers) system for the duration of camp.Syllabus\n\n\n\nSummer Stats Camp 2022: Social Network Analysis\n\n\nMonday\nJune 5\, 2023\n\n\n9:00 – 9:30\nWelcome and Introductions\n\n\n9:30 – 10:45\nTraditional vs. Social Network Data\n\n\n10:45 – 11:00\nRest Break\n\n\n11:00 – 12:30\nIntroduction to R\n\n\n12:30 – 1:30\nRest Break\n\n\n1:30 – 3:15\nLab demonstration: using R\n\n\n3:15 – 3:30\nRest Break\n\n\n3:30 – 5:00\nLab exercises\n\n\nTuesday \nJune 6\, 2023\n\n\n9:00 – 10:45\nSocial network statistics – from individual to network characteristics\n\n\n10:45 – 11:00\nRest Break\n\n\n11:00 – 12:30\nNetwork visualization\n\n\n12:30 – 1:30\nRest Break\n\n\n1:30 – 3:15\nLab demonstration: matrices\, network objects\, basic linear models\n\n\n3:15 – 3:30\nRest Break\n\n\n3:30 – 5:00\nLab exercises\n\n\nWednesday\nJune 7\, 2023\n\n\n9:00 – 10:45\nERG models for cross-sectional data\n\n\n10:45 – 11:00\nRest Break\n\n\n11:00 – 12:30\nERG model-building and interpretation\n\n\n12:30 – 1:30\nRest Break\n\n\n1:30 – 3:15\nLab demonstration: ERG model diagnostics\n\n\n3:15 – 3:30\nRest Break\n\n\n3:30 – 5:00\nLab exercises\n\n\nThursday\nJune 8\, 2023\n\n\n9:00 – 10:45\nSIENA models for longitudinal data\n\n\n10:45 – 11:00\nRest Break\n\n\n11:00 – 12:30\nSIENA model selection and parameter interpretation\n\n\n12:30 – 1:30\nRest Break\n\n\n1:30 – 3:15\nLab demonstration: evaluating model fit and troubleshooting in SIENA\n\n\n3:15 – 3:30\nRest Break\n\n\n3:30 – 5:00\nLab exercises\n\n\nFriday\nJune 9\, 2023\n\n\n9:00 – 10:45\nSIENA models of selection and influence\n\n\n10:45 – 11:00\nRest Break\n\n\n11:00 – 12:30\nAdvanced topics in social network analysis\n\n\n12:30 – 1:30\nRest Break\n\n\n1:30 – ~3:30\nIndividual Consultations\n\n\n\nDownload Sample Slides and Preview Course Video\nPlease fill out and submit the form below to get instant access to sample course materials.
URL:https://www.statscamp.org/courses/introduction-to-social-network-analysis-using-r-and-rsiena-seminar/
LOCATION:Embassy Suites – Albuquerque\, 1000 Woodward Place NE\, Albuquerque\, New Mexico\, 87102
CATEGORIES:Introduction to Social Network Analysis using R and Rsiena Seminar,Summer Camp
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Denver:20230605T090000
DTEND;TZID=America/Denver:20230609T170000
DTSTAMP:20221127T103931
CREATED:20220701T073144Z
LAST-MODIFIED:20221114T221134Z
UID:2659-1685955600-1686330000@www.statscamp.org
SUMMARY:Applied Latent Class Analysis & Finite Mixture Modeling
DESCRIPTION:IN PERSON – 5-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview:\nAn introduction to “person-centered” data analysis. Topics include latent class analysis\, latent class cluster analysis\, modeling predictors and outcomes of latent class membership\, and select extensions. Hands-on practice with Mplus is provided. \nThis five-day camp is an intensive short seminar in the fundamentals of finite mixture modeling. \nSeminar Topics:\n\nComing Soon…\n\nSeminar Description:\nFinite mixture models are a type of latent variable model that express the overall distribution of one or more variables as a mixture of a finite number of component distributions. In direct applications\, one assumes that the overall population heterogeneity with respect to a set of manifest variables is due to the existence of two or more distinct homogeneous subgroups\, or latent classes\, of individuals. These approaches are often termed “person-centered” analyses in contrast to the “variable-centered” analyses of conventional factor and SEM models. \nThis seminar will introduce participants to the prevailing “best practices” for direct applications of basic finite mixture modeling to cross-sectional data\, specifically latent profile analysis (LPA) also known as latent class cluster analysis (LCCA. s)\, in terms of model assumptions\, specification\, estimation\, evaluation\, selection\, and interpretation. Models that allow for the inclusion of correlates and predictors of latent class membership as well as distal outcomes of latent class membership will be presented. The seminar will also explore “hybrid” latent variable models that include both latent factors and latent classes (termed factor mixture models) and will touch briefly on some longitudinal extensions of mixture modeling\, as time allows (for a more in-depth treatment\, see the Stats Camp Session 2 seminar on longitudinal mixture modeling). The implementation of these models in the most recent version of the Mplus software will be demonstrated throughout the seminar. \n\nInstructor: Whitney Moore\, Ph.D.\n \nWhitney received her Ph.D. in the Psychosocial Aspects of Health and Physical Activity from the University of Kansas. She is currently an… Assistant Professor at Wayne State University in the Division of Kinesiology\, Health & Sport Studies where she teaches graduate courses in research methods\, sport and exercise psychology\, and statistics. \nRead More\n\n\nAPA Continuing Education Credits:\n \nThis course offers 29 hours of Continuing Education Credits. Stats Camp Foundation is approved by the American Psychological Association to sponsor continuing education for psychologists. Stats Camp Foundation maintains responsibility for this program and its content. \nSeminar Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\n\n\nLearning Objectives\nLearning Objectives:\nAfter engaging in course lectures and discussions as well as completing the hands-on practice activities with real data\, participants will be able to:\n\nDescribe mixture modeling in a general latent variable framework.\nPerform binary\, ordinal\, and multinomial logistic regression.\nUtilize Mplus to evaluate generalized linear models.\nIdentify best practice methods to enumerate latent classes for latent class analysis.\nPerform latent class regression.\nConduct measurement invariance in a latent class analysis framework.\nDerive distal outcomes in latent class analysis.\nPerform structural equation mixture modeling.\nDescribe the process of finite mixture modeling\nEnumerate latent classes in a finite mixture modeling framework.\nConduct finite mixture modeling with non-normal indicators.\nDescribe advanced topics and applications of mixture modeling\, such as multilevel latent class analysis.\n\nThis seminar is intended to give participants the knowledge and understanding necessary to identify and effectively execute “person-centered” analysis strategies using Mplus that might be most appropriate for their research questions. The seminar is also intended to provide a foundation for future learning about mixture modeling and resources to guide such endeavors. \nSeminar Prerequisites\nSeminar Prerequisites:\nComing Soon… \nSoftware and Computer Support\nSoftware and Computer Support:\nParticipants should bring a laptop computer. Instruction will be provided for the methods using the most current version of Mplus (base program with mixture add-on or base program with combination add-on). Mplus is available for Windows\, Mac\, and Linux environments (www.statmodel.com). \nParticipants who do not have access to software will be given temporary access to the server that contains fully functioning versions of the recommended software. \nNote: We will also make use of Excel and R to do various post-processing summaries. \nParticipants will receive an electronic copy of all seminar materials\, including PowerPoint slides\, Mplus scripts\, output files\, relevant supporting documentation\, and recommended readings. \nSeminar Audience\nSeminar Audience:\nIf you are interested in learning “person-centered” statistical modeling techniques that can identify unobserved subgroups (latent classes) characterized by qualitative differences in observed multivariate outcome distributions\, this seminar is for you. You should have a good working knowledge of the principles and practice of multiple regression and elementary statistical inference. You will get the most out of the seminar if you already have experience with binary and multinomial logistic regression. You do not need to know matrix algebra\, likelihood theory\, or SEM\, although that knowledge would be beneficial. No previous knowledge of mixture modeling\, latent class analysis\, or Mplus is assumed. Participants from a variety of fields—including psychology\, education\, human development\, public health\, prevention science\, sociology\, marketing\, business\, biology\, medicine\, political science\, and communication—will benefit from the seminar. \nSeminar Files\nSeminar Files\nComing Soon… \nAll statistical software used at Stats Camp will be available\, free to participants\, on our SMORS (statistical modeling on remote servers) system for the duration of camp.Syllabus\n\n\n\nSummer Stats Camp 2023: LCA\n\n\nMonday\nJune 5\, 2023\n\n\n9:00-9:30\nWelcome and introductions\n\n\n9:30-10:45\nOverview of mixture modeling in a general latent variable framework\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nReview of binary\, ordinal\, and multinomial logistic regression\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:15\nIntroduction to Mplus\n\n\n3:15-3:30\nRest Break\n\n\n3:30-5:00\nGeneralized linear modeling in Mplus\n\n\nTuesday\nJune 6\, 2023\n\n\n9:00-9:30\nQ & A\n\n\n9:30-10:45\nIntroduction to Latent Class Analysis (LCA)\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nLatent class enumeration for LCA\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:15\nLatent class enumeration (continued)\n\n\n3:15-3:30\nRest Break\n\n\n3:30-5:00\nIntroduction to Latent Class Regression (LCR)\n\n\nWednesday\nJune 7\, 2023\n\n\n9:00-9:30\nQ & A\n\n\n9:30-10:45\nLCR (continued)\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nMeasurement invariance in LCA\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:15\nDistal outcomes in LCA\n\n\n3:15-3:30\nRest Break\n\n\n3:30-5:00\nStructural equation mixture modeling\n\n\nThursday\nJune 8\, 2023\n\n\n9:00-9:30\nQ & A\n\n\n9:30-10:45\nIntroduction to Finite Mixture Modeling (FMM)\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nLatent class enumeration for FMM\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:15\nLatent class enumeration for FMM (continued)\n\n\n3:15-3:30\nRest Break\n\n\n3:30-5:00\nFMM with non-Normal indicators\n\n\nFriday\nJune 9\, 2023\n\n\n9:00-9:30\nQ & A\n\n\n9:30-10:45\nOverview of “hybrid” factor mixture models (including growth mixture models)\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nOverview of advanced topics in mixture modeling (e.g.\, multilevel LCA)\n\n\n12:30-1:30\nRest Break\n\n\n1:30~4:30\nIndividual consultations\n\n\n\nDownload Sample Slides and Preview Course Video\nPlease fill out and submit the form below to get instant access to sample course materials.
URL:https://www.statscamp.org/courses/applied-latent-class-analysis-finite-mixture-modeling/
LOCATION:Embassy Suites – Albuquerque\, 1000 Woodward Place NE\, Albuquerque\, New Mexico\, 87102
CATEGORIES:Applied Latent Class Analysis & Finite Mixture Modeling,Summer Camp
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DTSTART;TZID=America/Denver:20230605T090000
DTEND;TZID=America/Denver:20230609T170000
DTSTAMP:20221127T103931
CREATED:20220701T075623Z
LAST-MODIFIED:20221114T220917Z
UID:2670-1685955600-1686330000@www.statscamp.org
SUMMARY:Multilevel Modeling
DESCRIPTION:IN PERSON – 5-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview:\nAn intermediate 5-day course introducing multilevel modeling for analyzing hierarchically organized data. Everything is nested\, so you need something more than multiple regression or analysis of variance to get the job done! Nested data structures can include students within classrooms\, professionals within corporations\, patients within hospitals\, or repeated observations from the same person. Multilevel modeling (MLM) is built to handle this kind of data. You will use real datasets and the R software environment to learn how to analyze multilevel data sets and interpret results of multilevel models. \nSeminar Topics:\n\nReview of regression and methods of handling nested data\nRandom-intercept and random-slope models\nTesting and interpreting interactions in multilevel models\nCross-sectional and Longitudinal multilevel models\nMultilevel models for binary outcomes\nCross-classified random effects modeling\n\nNote: MLM is sometimes referred to as mixed-effects modeling\, hierarchical linear modeling\, or random coefficients modeling. This course will focus primarily on with a single outcome variable. As such\, this course (https://www.statscamp.org/courin combination with a course in SEM Foundations) would provide an ideal introduction to the foundations necessary to prepare for the advanced Summer Stats Camp course\, Multilevel SEM with xxM. \nSeminar Description:\nThis course is designed to provide theoretical and applied understandings of multilevel modeling. The fundamentals of multilevel modeling are taught by extending knowledge of regression analyses to designs involving a nested data structure. Nested data structures include\, for example\, students within classrooms\, professionals within corporations\, patients within hospitals\, or repeated observations from the same person. In each of these cases and many more\, the data are hierarchically arranged and may require methods beyond multiple regression or analysis of variance. These methods fall under the heading of multilevel modeling\, which is also sometimes referred to as mixed modeling\, hierarchical linear modeling\, or random coefficients modeling. \nThis course will help you begin to learn how to analyze multilevel data sets and interpret results of multilevel modeling analyses. Cross-sectional and longitudinal models\, the most common multilevel modeling applications\, are featured in the seminar. Using real datasets provided in the seminar\, participants will learn how to use the R software program to analyze data and interpret results. Further\, the course will emphasize proper interpretation of analysis results and illustrate procedures that can be used to specify multilevel models. Coverage of multilevel models for binary outcomes and cross-classified random effects modeling will also be included. \nParticipants will receive an electronic copy of all course materials\, including lecture slides\, practice datasets\, software scripts\, relevant supporting documentation\, and recommended readings. Participants will also have access to a video recording of the course. \n\nInstructor: Alex Schoemann\, Ph.D.\n \nDr. Alexander M. Schoemann\, is an Associate Professor of Psychology at East Carolina University. Alex received his PhD from the University of Kansas in 2011 in Social and Quantitative Psychology under the mentorship of Dr. Kristopher Preacher. He has been a Stats Camp instructor since 2012 (after spending several years as a “counselor”). Alex teaches graduate courses in research design\, regression\, multivariate statistics\, structural equation modeling and multilevel modeling.… His research is focused on applying advanced quantitative methods to data from behavior sciences. Specific topics of interest include mediation and moderation\, power analyses\, missing data estimation\, meta-analysis\, structural equation models and multilevel models. Alex is also interested in developing user friendly software for advanced methods including applications for power analysis for mediation models (http://marlab.org/power_mediation/). \nRead More\n\n\nAPA Continuing Education Credits:\n \nThis course offers 29 hours of Continuing Education Credits. Stats Camp Foundation is approved by the American Psychological Association to sponsor continuing education for psychologists. Stats Camp Foundation maintains responsibility for this program and its content. \nSeminar Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\n\n\nLearning Objectives\nLearning Objectives:\nAfter engaging in course lectures and discussions as well as completing the hands-on practice activities with real data\, participants will be able to:\n\nIdentify the basic functions of R that are relevant to multilevel modeling.\nUnderstand the principles behind multiple linear regression.\nDescribe the methods of handling nested data.\nModify regression models by adding predictors and random effects.\nApply methods of centering.\nEvaluate models with interactions.\nConduct multiparameter tests.\nMake informed decisions about model selection.\nEvaluate longitudinal models.\nImplement alternative error structures.\nEvaluate multiple group models.\nUnderstand the roles sample size and power play in a multilevel framework.\nEvaluate multivariate models.\nEvaluate three-level models.\nEvaluate cross-classified random effects models.\nEvaluate models with categorical outcome variables.\n\nSeminar Prerequisites\nSeminar Prerequisites:\nRequired: \n\nAdvanced proficiency in multiple linear regression\, including use of categorical independent variables\nIntermediate fluency with statistical software (e.g. SAS\, SPSS\, or R) which will aid in the use of R (Note that materials for introducing attendees to R software will be shared in advance and the course will begin with a short introduction to R).\n\nNot required but advantageous: \n\nAt least limited experience (e.g.\, graduate-level course) with multivariate data analysis.\nAt least limited experience in binary logistic regression\nAt least limited experience using R\n\nNo level of proficiency beyond basic awareness is assumed for skills related to: \n\nMultilevel Modeling.\nAdvanced mathematical or statistical topics such as matrix algebra or likelihood theory.\n\nSoftware and Computer Support\nSoftware and Computer Support:\nParticipants need to bring a laptop computer with Wi-Fi capabilities. \nAll statistical software used at Stats Camp will be available\, free to participants\, on our SMORS (statistical modeling on remote servers) system for the duration of camp. \nNote\, however\, that R and RStudio are the software programs that will be demonstrated. Both programs are free and can be downloaded from https://cloud.r-project.org/ and https://www.rstudio.com/products/rstudio/download/\, respectively. Additional directions will be shared with enrolled participants. \nNote: Limited examples will also be provided in SPSS and SAS but the majority of the course will be taught using R. \nSeminar Audience\nSeminar Audience:\nComing Soon… \nSeminar Files\nSeminar Files\nBelow are links to seminar files for those who enrolled in the seminar. Please download these files onto your computer before the first day of the seminar. The files are password protected to respect the intellectual property rights of the instructors. By using your login information you agree not to share your login information or the content protected by it. \nInstructor will provide password on first day of seminar:\nClick Here to Access The Multilevel Modeling Seminar Files \nAll statistical software used at Stats Camp will be available\, free to participants\, on our SMORS (statistical modeling on remote servers) system for the duration of camp.Syllabus\n\n\n\nMonday\nJune 5\, 2023\n\n\n9:00-9:30\nWelcome and introductions\n\n\n9:30-10:45\nIntroduction to multilevel modeling and basics of R\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nReview of multiple linear regression\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nMethods of handling nested data\n\n\n3:00-3:15\nRest Break\n\n\n3:15-5:00\nAdding predictors and random effects\n\n\nTuesday\nJune 6\, 2023\n\n\n9:00-10:45\nCentering\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nInteractions and contextual effects\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nEstimation\n\n\n3:00-3:15\nRest Break\n\n\n3:15-5:00\nMultiparameter tests and model selection\n\n\nWednesday\nJune7\, 2023\n\n\n9:00-10:45\nLongitudinal models\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nLongitudinal models (continued)\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nAlternative error structures\n\n\n3:00-3:15\nRest Break\n\n\n3:15-5:00\nMultiple group models\n\n\nThursday\nJune 8\, 2023\n\n\n9:00-10:45\nPower and sample size\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nMultivariate Models\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nThree-level modeling\n\n\n3:00-3:15\nRest Break\n\n\n3:15-5:00\nOne-on-one consultations with instructor\n\n\nFriday\nJune 9\, 2023\n\n\n9:00-10:45\nCross-classified random effects modeling\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nCross-classified random effects modeling (continued)\n\n\n12:30-1:30\nRest Break\n\n\n1:30-5:00\nOne-on-one consultations with instructor\n\n\n\nDownload Sample Slides and Preview Course Video\nPlease fill out and submit the form below to get instant access to sample course materials.
URL:https://www.statscamp.org/courses/multilevel-modeling/
LOCATION:Embassy Suites – Albuquerque\, 1000 Woodward Place NE\, Albuquerque\, New Mexico\, 87102
CATEGORIES:Multilevel Modeling,Summer Camp
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DTSTART;TZID=America/Denver:20230605T090000
DTEND;TZID=America/Denver:20230609T170000
DTSTAMP:20221127T103931
CREATED:20220701T081220Z
LAST-MODIFIED:20220921T032946Z
UID:2673-1685955600-1686330000@www.statscamp.org
SUMMARY:Program Evaluation and Cost-Benefit Analysis
DESCRIPTION:IN PERSON – 5-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview:\nTrying to get that grant or contract?! You need to have a rigorous\, evidence-based plan to secure that funding. Almost all non-scientific evaluations will be unawarded or quickly dismissed. You need to assess your research project or program by conducting a rigorous evaluation of it. Fields such as sociology\, psychology\, education\, human development\, marketing\, business\, biology\, medicine\, political science\, communication\, governmental and nonprofit agencies\, will benefit from the seminar. \nSeminar Topics:\n\nProgram theory and Logic Models\nNeeds assessment\nProject Budgeting\nMonitoring progress and measuring outcomes\nCost-effectiveness vs. Cost-benefit analysis\nTime and discounting\nSensitivity analysis and risk analysis\nSem and decision tree extensions of cost-benefit analysis.\nReporting writing\n\nSeminar Description:\nFields such as sociology\, psychology\, education\, human development\, marketing\, business\, biology\, medicine\, political science\, communication\, governmental and nonprofit agencies\, will benefit from the seminar. \n\nInstructor: Mwarumba Mwavita\, Ph.D.\n \nMwarumba Mwavita is Director of the Center for Educational Research and Evaluation (CERE) at Oklahoma State University. In addition\, he is an Assistant Professor in the Research\, …Evaluation\, Measurement and Statistics (REMS) program in the School of Educational Studies (SES). \nRead More\n\n\n\nAPA Continuing Education Credits:\n \nThis course offers 29 hours of Continuing Education Credits. Stats Camp Foundation is approved by the American Psychological Association to sponsor continuing education for psychologists. Stats Camp Foundation maintains responsibility for this program and its content. \n\nSeminar Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\n\n\nLearning Objectives\nLearning Objectives:\nAfter engaging in course lectures and discussions as well as completing the hands-on practice activities with real data\, participants will be able to:\n\nDescribe the roles Stakeholder and Evaluator engagement play in developing an evaluation plan.\nDevelop evaluation questions.\nImplement needs assessment and program planning.\nDescribe how to formulate and assess program theory.\nMonitor program process.\nMeasure outcomes using program evaluation methods\nDescribe cost-benefit and cost-effectiveness.\nImplement resource allocation and decision-making strategies.\nPerform the steps in a cost-benefit analysis.\nEvaluate whose viewpoint is being analyzed.\nMeasure value costs and benefits\nEvaluate time values and decision rules.\nPerform a sensitivity analysis.\nEvaluate uncertainty and risk.\nPerform a risk analysis\nDescribe the structural equation modeling extensions of a cost-benefit analysis.\nImplement decision tree methods in a cost-benefit analysis framework.\n\nSeminar Prerequisites\nSeminar Prerequisites:\nComing Soon… \nSoftware and Computer Support\nSoftware and Computer Support:\nComing Soon… \nSeminar Audience\nSeminar Audience:\nIf you need to analyze a program or to conduct cost-effectiveness or cost-benefit analyses\, this seminar is for you. You should have a good working knowledge of the principles and practice of elementary statistical inference. You do not need to know matrix algebra\, calculus\, or decision theory (although knowledge of decision theory might be beneficial). Participants from a variety of fields\, including sociology\, psychology\, education\, human development\, marketing\, business\, biology\, medicine\, political science\, communication\, and governmental and nonprofit agencies\, will benefit from the seminar. \nSeminar Files\nSeminar Files\nFor those who have enrolled in the seminar please download these files onto your computer before the first day of the seminar. The files are password protected to respect the intellectual property rights of the instructors. By using your login information you agree not to share your login information or the content protected by it. \nInstructor will provide password on first day of seminar. \nAll statistical software used at Stats Camp will be available\, free to participants\, on our SMORS (statistical modeling on remote servers) system for the duration of camp.Syllabus\n \n\n\n\nSummer Stats Camp 2023: Program Evaluation and Cost-Benefit Analysis\n\n\n\nMonday\nJune 5\, 2023\n\n\n9:00–10:45\nWelcome and Introduction to Program Evaluation\n\n\n10:45–11:00\nSnack and Rest Break\n\n\n11:00–12:30\nFocusing Evaluation: Stakeholders and Evaluator Engagement in Developing and Evaluation Plan\n\n\n12:30–1:30\nLunch Break\n\n\n1:30–3:15\nProcess of Program Evaluation: Formulating Evaluation Questions\n\n\n3:15–3:30\nSnack and Rest Break\n\n\n3:30–5:00\nNeeds Assessment and Program Planning\n\n\nTuesday\nJune 6\, 2023\n\n\n9:00–10:45\nProgram Theory: How to Formulate and Access Program Theory\n\n\n10:45–11:00\nSnack and Rest Break\n\n\n11:00–12:30\nProgram Monitoring Process\n\n\n12:30–1:30\nLunch Break\n\n\n1:30–3:15\nMeasuring Outcomes; Methods in Program Evaluation\n\n\n3:15–3:30\nSnack and Rest Break\n\n\n3:30–5:00\nResults and Interpretations in Program Evaluation\n\n\nWednesday\nJune 7\, 2023\n\n\n9:00–10:45\nIntroduction to Cost Benefits and Cost Effectiveness\n\n\n10:45–11:00\nSnack and Rest Break\n\n\n11:00–12:30\nRecourse Allocation and Decision Making\n\n\n12:30–1:30\nLunch Break\n\n\n1:30–3:15\nSteps in Cost Benefit Analysis\n\n\n3:15–3:30\nSnack and Rest Break\n\n\n3:30–5:00\nWhose Viewpoint?\n\n\nThursday\nJune 8\, 2023\n\n\n9:00–10:45\nMeasuring and Valuing Cost and Benefits\n\n\n10:45–11:00\nSnack and Rest Break\n\n\n11:00–12:30\nTime Values and Decision Rules\n\n\n12:30–1:30\nLunch Break\n\n\n1:30–3:15\nSensitivity Analysis\n\n\n3:15–3:30\nSnack and Rest Break\n\n\n3:30–5:00\nUncertainty\, Risk and Risk Analysis\n\n\nFriday\nJune 9\, 2023\n\n\n9:00–10:45\nSEM Extensions and Cost-Benefit Analysis\n\n\n10:45–11:00\nSnack and Rest Break\n\n\n11:00–12:30\nDecision Tree Extensions and Cost-Benefit Analysis\n\n\n12:30–1:30\nSnack and Rest Break\n\n\n1:30~3:30\nIndividual Consultations\n\n\n\n \nDownload Sample Slides and Preview Course Video\nPlease fill out and submit the form below to get instant access to sample course materials.
URL:https://www.statscamp.org/courses/program-evaluation-and-cost-benefit-analysis/
LOCATION:Embassy Suites – Albuquerque\, 1000 Woodward Place NE\, Albuquerque\, New Mexico\, 87102
CATEGORIES:Program Evaluation and Cost-Benefit Analysis,Summer Camp
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DTSTART;TZID=America/Denver:20230605T090000
DTEND;TZID=America/Denver:20230609T170000
DTSTAMP:20221127T103931
CREATED:20220701T085536Z
LAST-MODIFIED:20221117T171059Z
UID:2684-1685955600-1686330000@www.statscamp.org
SUMMARY:SEM Foundations & Extended Applications
DESCRIPTION:IN PERSON – 5-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview:\n\nDo you want to take your measurement to the latent level? Well\, this is it\, you have found it\, the foundation to what you need to know for latent variable modeling – structural equation modeling (SEM)! Most campers report their prior training was insufficient and/or outdated. We will introduce you to the current techniques and advances in SEM as well as guide you through the steps to ‘craft’ an exquisite SEM model. \n\nSeminar Topics:\n\nPhantom Constructs\nFitting measurement models\nThree methods of scale setting – including effects coding!\nUpdated recommendations for Scale Validation\nMultiple-Group Comparisons with applications for experimental and observational groups!\nFactorial/Measurement Invariance – Are you measuring the same construct?\nExtended Applications Such as Parceling and Missing Data\nMediation and Indirect Effects using Bootstrapping\nModeration\, creating latent interaction terms!\n\nSeminar Description:\nThis summer institute is an intensive short seminar on the principles of structural equation modeling. \n\nInstructor: Todd D. Little\, Ph.D.\n \nTodd D. Little\, PhD is a Professor and director of the Institute for Measurement\, Methodology\, Analysis and Policy at Texas Tech University. He is widely recognized for his quantitative work on various aspects of applied SEM (e.g.\, modern missing data treatments\, indicator selection\, parceling\, modeling developmental processes) as well as his substantive developmental research (e.g.\, action-control processes and motivation\, coping\, and self-regulation). His work has garnered over 49\,424 …citations with an h-index of 97 and an i10-index of 261. In 2001\, he was elected to membership in the Society for Multivariate Experimental Psychology\, and in 2009\, he was elected President of APA’s Division 5 (Evaluation\, Measurement\, and Statistics). He is a fellow in APA\, APS\, and AAAS. In 2013\, he received the Cohen award from Division 5 of APA for distinguished contributions to teaching and mentoring and in 2015 he received the inaugural distinguished contributions award for mentoring developmental scientists from the Society for Research in Child Development. Both awards cited his founding of Stats Camp (Statscamp.org) in 2003 and its ongoing impact on shaping the quality of scientific inquiry for both past and future generations of researchers. Download Todd’s CV (PDF) \nRead More\n\n\n\nInstructor: Elizabeth Grandfield\, Ph.D.\n \nElizabeth received her Ph.D. in Quantitative Psychology at the University of Kansas. She is currently an Assistant Professor in the Department of Methodology and Statistics at Utrecht University in the Netherlands. Her research focuses on evaluating measurement invariance with an emphasis in longitudinal designs. In areas of applied research\, Elizabeth has been involved in longitudinal children studies at Juniper Gardens as well as a national nursing study at Kansas University Medical Center\, both in Kansas City. She also received… the 2011 Multivariate Software Award\, presented by Peter Bentler and Eric Wu. Elizabeth has been involved in Stats Camp since 2012. \nRead More\n\n\nInstructor: Zachary Stickley\, Ph.D.\n \nZachary is a researcher in the College of Education at Texas Tech University studying latent variable modeling and planned missing data. He received his Master of Education degree from Texas Tech University and his Bachelor of Science in Psychology from Tarleton State University.… He has assisted Dr. Little in the instruction of Structural Equation Modeling courses at Texas Tech University as well as at numerous Stats Camp seminars and analysis retreats. \nRead More\n\n\nAPA Continuing Education Credits:\n \nThis course offers 26 hours of Continuing Education Credits. Stats Camp Foundation is approved by the American Psychological Association to sponsor continuing education for psychologists. Stats Camp Foundation maintains responsibility for this program and its content. \n\nSeminar Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\nLearning Objectives\nLearning Objectives:\nThe five-day training institute on Structural Equation Modeling will enable participants to: \n\nDescribe the psychometric properties that underly Structural Equation Modeling (SEM).\nDefine a latent construct using manifest variables.\nIdentify a latent construct using numerous methods of identification\, including marker method\, fixed factor\, and effects coding.\nConduct confirmatory factor analysis (CFA) and evaluate model fit using several fit indices.\nCompare CFA models using several comparison metrics.\nGenerate and implement item parceling schemes.\nEvaluate multiple groups using the CFA framework using weak and strong invariance.\nTest and compare latent parameters in a multiple group framework.\nEvaluate and address missing data with both FIML and Multiple Imputation.\nImplement a planned missing data design.\nEvaluate mediation and moderation in an SEM framework.\nEvaluate multi-trait\, multi-method (MTMM) models.\nEvaluate hierarchical models.\n\nSeminar Prerequisites\nSeminar Prerequisites:\nComing Soon… \nSoftware and Computer Support\nSoftware and Computer Support:\nComing Soon… \nSeminar Audience\nSeminar Audience:\nIf you need to analyze the covariance structure of multivariate data and have a basic statistical background\, this seminar is for you. You should have a good working knowledge of the principles and practice of multiple regression and elementary statistical inference. You do not need to know matrix algebra\, calculus\, or likelihood theory (although that knowledge would be beneficial). Participants from a variety of fields\, including sociology\, psychology\, education\, human development\, marketing\, business\, biology\, medicine\, political science\, and communication\, will benefit from the seminar. \nThe seminar will support LISREL\, Mplus or Laavan. Some assistance will be available for questions related to other structural modeling packages. No previous knowledge of LISREL\, Mplus or Laavan is assumed. Furthermore\, nearly all the techniques taught in the seminar can be translated fairly easily to most other packages. \nSeminar Files\nSeminar Files\nBelow are links to seminar files for those who enrolled in the seminar. Please download these files onto your computer before the first day of the seminar. The files are password protected to respect the intellectual property rights of the instructors. By using your login information you agree not to share your login information or the content protected by it. \nInstructor Will Provide Password on First Day of Seminar:\nClick Here to View Seminar Materials Page for SEM Foundations and Extended Applications \nAll statistical software used at Stats Camp will be available\, free to participants\, on our SMORS (statistical modeling on remote servers) system for the duration of camp.Syllabus\n\n\n\nSummer Stats Camp 2023: SEM Foundations\n\n\nMonday\nJune 5\, 2023\n\n\n9:00 – 10:45\nWelcome and Introductions. Philosophy\n\n\n10:45 – 11:00\nRest Break\n\n\n11:00 – 12:30\nPsychometrics\n\n\n12:30 – 1:30\nRest Break\n\n\n1:30 – 3:15\nDefining Constructs\n\n\n3:15 – 3:30\nRest Break\n\n\n3:30 – 5:00\nIdentification\n\n\nTuesday \nJune 6\, 2023\n\n\n9:00 – 10:45\nConfirmatory Factor Analysis I – Introduction to CFA\n\n\n10:45 – 11:00\nRest Break\n\n\n11:00 – 12:30\nConfirmatory Factor Analysis II – Comparing Models\, Model fit\n\n\n12:30 – 1:30\nRest Break\n\n\n1:30 – 3:15\nCFA: The foundation of any SEM model\, continued\n\n\n3:15 – 3:30\nRest Break\n\n\n3:30 – 5:00\nParcels and Parceling; Start of Individual Consultations\n\n\nWednesday\nJune 7\, 2023\n\n\n9:00 – 10:45\nMultiple-Group CFA – Testing for configural and weak invariance\n\n\n10:45 – 11:00\nRest Break\n\n\n11:00 – 12:30\nMultiple-Group CFA – Testing for Strong Invariance\n\n\n12:30 – 1:30\nRest Break\n\n\n1:30 – 3:15\nMultiple-Group CFA – Tests and comparing latent parameters\n\n\n3:15 – 3:30\nRest Break\n\n\n3:30 – 5:00\nWrap-up then Individual Consultations\n\n\nThursday\nJune 8\, 2023\n\n\n9:00 – 10:45\nMissing data and Power\n\n\n10:45 – 11:00\nRest Break\n\n\n11:00 – 12:30\nMultiple-Group SEM and Latent Regression Models\n\n\n12:30 – 1:30\nRest Break\n\n\n1:30 – 3:15\nMediation and Moderation\n\n\n3:15 – 3:30\nRest Break\n\n\n3:30 – 5:00\nCatch-up time then Individual Consultations\n\n\nFriday\nJune 9\, 2023\n\n\n9:00 – 10:45\nMulti-trait\, Multi-Method (MTMM) and Hierarchical Models\n\n\n10:45 – 11:00\nRest Break\n\n\n11:00 – 12:30\nHierarchical Models\, continued; Writing results\, Cautions & Wrap-up\n\n\n12:30 – 1:30\nRest Break\n\n\n1:30 – ~3:30\nIndividual Consultations\n\n\n\nDownload Sample Slides and Preview Course Video\nPlease fill out and submit the form below to get instant access to sample course materials.
URL:https://www.statscamp.org/courses/sem-foundations-extended-applications/
LOCATION:Embassy Suites – Albuquerque\, 1000 Woodward Place NE\, Albuquerque\, New Mexico\, 87102
CATEGORIES:SEM Foundations & Extended Applications,Summer Camp
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BEGIN:VEVENT
DTSTART;TZID=America/Denver:20230612T090000
DTEND;TZID=America/Denver:20230616T170000
DTSTAMP:20221127T103931
CREATED:20220701T090804Z
LAST-MODIFIED:20221114T220231Z
UID:2690-1686560400-1686934800@www.statscamp.org
SUMMARY:Intro to Data Mining and Machine Learning
DESCRIPTION:IN PERSON – 5-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview:\nAn intermediate 5-day course introducing several popular data mining approaches such as regression based methods (ridge and lasso regularized regression\, regression splines)\, tree methods (random forests\, boosted trees)\, and support vector machines\, and their application to empirical data. The course combines lectures and hands-on practice using R. \nSeminar Topics:\n\nReview of linear regression and the least squares criterion\nRegularization methods (ridge regression\, lasso\, elastic net)\nRegression splines\nPrediction error and k-fold cross validation\nTree methods to predict categorical or continuous outcomes (CART\, random forest\, boosting\nSupport vector machines for classification\n\nSeminar Description:\nIn the age of rapidly increasing data collection endeavors it has become more and more important to understand how to find structure in data\, especially when substantive theory about structural relations between the collected variables is not yet fully developed. This short course starts with briefly outlining the key differences and similarities between standard parametric modeling (e.g.\, linear regression) and data mining approaches. The course provides basic insights into a number of popular methods such as regression methods (ridge regression and the lasso\, regression splines)\, tree methods (CART\, random forests\, boosting)\, and support vector machines. The emphasis is on a conceptual understanding of these methods and their appropriate application to empirical data. Importantly\, these methods are useful not only for large data collections\, but also more generally for exploratory analyses when the substantive theory to design and fit suitable parametric models (e.g. SEM) is not available. Data mining (aka statistical learning) is used in a wide variety of fields including but not limited to public health\, education\, biology\, and the different social sciences. \nParticipants are invited to discuss potential data mining applications to their particular field of interest during individual consultations with the instructor scheduled at the end of the second and third day. \nParticipants will receive an electronic copy of all course materials\, including lecture slides\, practice datasets\, software scripts\, relevant supporting documentation\, and recommended readings. Participants will also have access to a video recording of the course. \n\nInstructor: Gitta Lubke\, Ph.D.\nGitta Lubke is a Full Professor in the Department of Psychology/Quantitative Area at the University of Notre Dame. Her research interests are in data mining and general latent variable modeling. In addition to the challenges of analysing complex human behavior such as psychiatric disorders\, she is interested in the analysis of genetic data. Related areas of expertise include mixture models\, twin models\, multi-group factor analysis and measurement invariance\, longitudinal analyses\, and the analysis of categorical data. \nAPA Continuing Education Credits:\n \nThis course offers 29 hours of Continuing Education Credits. Stats Camp Foundation is approved by the American Psychological Association to sponsor continuing education for psychologists. Stats Camp Foundation maintains responsibility for this program and its content. \nSeminar Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\n\n\nLearning Objectives\nLearning Objectives:\nAfter engaging in course lectures and discussions as well as completing the hands-on practice activities with real data\, participants will be able to:\n\nUnderstand some of the key differences and similarities between parametric modeling and data mining methods.\nExpand the acquired basic knowledge of several popular data mining methods and apply these methods to empirical data.\nUtilize linear and multiple regression to categorize data.\nImplement ridge regression and Lasso.\nAssess and interpret the results of empirical analyses through k-fold cross validation and computation of prediction errors.\nImplement and evaluate regression splines.\nImplement and evaluate decision trees to categorize data.\nUtilize CART and bagging techniques.\nImplement random forests to evaluate data.\nImplement and evaluate boosted trees.\nUnderstand and utilize support vector machines to evaluate data.\nUtilize R packages for data mining.\nUnderstand and evaluate scientific papers covering data mining applications to empirical data.\n\nSeminar Prerequisites\nSeminar Prerequisites:\nRequired: \n\nadvanced proficiency in linear regression\, including the estimation of regression coefficients using least squares\nintermediate familiarity with iterative optimization (e.g. how to use the Newton-Raphson algorithm to find a maximum)\nIntermediate proficiency with R\nIntermediate knowledge of exploratory data analysis\n\nNot required but advantageous: \n\nAt least limited experience (e.g.\, graduate-level course) in calculus\nUnderstanding the relation between multiple testing and Type I error\, and\, more generally\, the challenges of finding relevant predictors in large data sets\n\nNo level of proficiency beyond basic awareness is assumed for skills related to: \n\nData mining methods\nMore advanced mathematical or statistical topics such constrained estimation (e.g.\, using Laplace multipliers)\n\nSoftware and Computer Support\nSoftware and Computer Support:\nParticipants need to bring a laptop computer with Wi-Fi capabilities. \nAll statistical software used at Stats Camp will be available\, free to participants\, on our SMORS (statistical modeling on remote servers) system for the duration of camp. \nAll instruction for this course will be based on the freely available software program R. Please make sure to have a recent version installed. \nSeminar Audience\nSeminar Audience:\nComing Soon… \nCourse Learning Goals\nCourse Learning Goals:\nAfter engaging in course lectures and discussions as well as completing the hands-on practice activities with empirical data\, participants will be able to: \n\nUnderstand some of the key differences and similarities between parametric modeling and data mining methods\nExpand the acquired basic knowledge of several popular data mining methods and apply these methods to empirical data\nAssess and interpret the results of empirical analyses through k-fold cross validation and computation of prediction errors\nUtilize R packages for data mining\nUnderstand and evaluate scientific papers covering data mining applications to empirical data\n\nSeminar Files\nSeminar Files\nSeminar files will be provided by the instructor on the first day of the seminar. You do not need to download anything prior to the event date. All materials will be provided during or after the class. \nAll statistical software used at Stats Camp will be available\, free to participants\, on our SMORS (statistical modeling on remote servers) system for the duration of camp.Syllabus\n\n\n\nMonday\nJune 12\, 2023\n\n\n9:00-9:30\nWelcome and introductions\n\n\n9:30-10:45\nSimple and Multiple Linear Regression\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nRidge Regression and Lasso\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nApplication of Ridge Regression and Lasso\n\n\n3:00-3:15\nRest Break\n\n\n3:15-5:00\nPrediction Error and Cross Validation\n\n\nTuesday\nJune 13\, 2023\n\n\n9:00-10:45\nRegression Splines\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nApplication of Regression Splines\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nIntroduction to Tree Methods\n\n\n3:00-3:15\nRest Break\n\n\n3:15-5:00\nIndividual consultation with instructor\n\n\nWednesday\nJune 14\, 2023\n\n\n9:00-10:45\nCART and bagging\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nRandom Forests\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nApplication of Random Forests\n\n\n3:00-3:15\nRest Break\n\n\n3:15-5:00\nIndividual consultation with instructor\n\n\nThursday\nJune 15\, 2023\n\n\n9:00-10:45\nBoosted Trees\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nBoosted Trees\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nApplication of Boosted Trees\n\n\n3:00-3:15\nRest Break\n\n\n3:15-5:00\nIndividual consultation with instructor\n\n\nFriday\nJune 16\, 2023\n\n\n9:00-10:45\nSupport Vector Machines\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nApplication of Support Vector Machines\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nIndividual consultation with instructor\n\n\n3:00-3:15\nRest Break\n\n\n3:15-5:00\nIndividual consultation with instructor\n\n\n\nDownload Sample Slides and Preview Course Video\nPlease fill out and submit the form below to get instant access to sample course materials.
URL:https://www.statscamp.org/courses/intro-to-data-mining-and-machine-learning/
LOCATION:Embassy Suites – Albuquerque\, 1000 Woodward Place NE\, Albuquerque\, New Mexico\, 87102
CATEGORIES:Intro to Data Mining and Machine Learning,Summer Camp
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DTSTART;TZID=America/Denver:20230612T090000
DTEND;TZID=America/Denver:20230616T170000
DTSTAMP:20221127T103931
CREATED:20220701T092234Z
LAST-MODIFIED:20221117T171020Z
UID:2697-1686560400-1686934800@www.statscamp.org
SUMMARY:Longitudinal Structural Equation Modeling (LSEM)
DESCRIPTION:IN PERSON – 5-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview:\nDo you have repeated measurements? Have you collected data over multiple timepoints? Do you need help designing your longitudinal study? If so\, this is your course! Let us help you appropriately design your longitudinal study and analyze your data in the SEM latent variable framework using Longitudinal Structural Equation Modeling (LSEM). This framework will allow you more flexibility in evaluating your research questions over time as well as test assumptions that traditional techniques like ANOVA ignore. \nSeminar Topics:\n\nDesign and measurement issues in cross-sectional and longitudinal research\nTraditional panel designs\nOverview of missing data\nLatent growth curve modeling\nTesting for Mediation and Moderation\nUsing Phantom Constructs\nLongitudinal Measurement Invariance – Multiple Group LSEM\nGrowth Mixture Models\n\nSeminar Description:\nThe seminar will be a series of lectures and computer workshops to provide participants with advanced training in the use of SEM for the analysis of longitudinal data. \n\nInstructor: Todd D. Little\, Ph.D.\n \nTodd D. Little\, PhD is a Professor and director of the Institute for Measurement\, Methodology\, Analysis and Policy at Texas Tech University. He is widely recognized for his quantitative work on various aspects of applied SEM (e.g.\, modern missing data treatments\, indicator selection\, parceling\, modeling developmental processes) as well as his substantive developmental research (e.g.\, action-control processes and motivation\, coping\, and self-regulation). His work has garnered over 49\,424 …citations with an h-index of 97 and an i10-index of 261. In 2001\, he was elected to membership in the Society for Multivariate Experimental Psychology\, and in 2009\, he was elected President of APA’s Division 5 (Evaluation\, Measurement\, and Statistics). He is a fellow in APA\, APS\, and AAAS. In 2013\, he received the Cohen award from Division 5 of APA for distinguished contributions to teaching and mentoring and in 2015 he received the inaugural distinguished contributions award for mentoring developmental scientists from the Society for Research in Child Development. Both awards cited his founding of Stats Camp (Statscamp.org) in 2003 and its ongoing impact on shaping the quality of scientific inquiry for both past and future generations of researchers. Download Todd’s CV (PDF) \nRead More\n\n\n\nInstructor: Whitney Moore\, Ph.D.\n \nWhitney received her Ph.D. in the Psychosocial Aspects of Health and Physical Activity from the University of Kansas. She is currently an… Assistant Professor at Wayne State University in the Division of Kinesiology\, Health & Sport Studies where she teaches graduate courses in research methods\, sport and exercise psychology\, and statistics. \nRead More\n\n\nInstructor: Zachary Stickley\, Ph.D.\n \nZachary is a researcher in the College of Education at Texas Tech University studying latent variable modeling and planned missing data. He received his Master of Education degree from Texas Tech University and his Bachelor of Science in Psychology from Tarleton State University.… He has assisted Dr. Little in the instruction of Structural Equation Modeling courses at Texas Tech University as well as at numerous Stats Camp seminars and analysis retreats. \nRead More\n\n\n\nAPA Continuing Education Credits:\n \nThis course offers 29 hours of Continuing Education Credits. Stats Camp Foundation is approved by the American Psychological Association to sponsor continuing education for psychologists. Stats Camp Foundation maintains responsibility for this program and its content. \n\nSeminar Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\n\nLearning Objectives\nLearning Objectives:\nThe five-day training institute on Longitudinal SEM will enable participants to:\n\nAddress design and measurement issues in longitudinal modeling.\nAcquire understanding of the SEM concepts that are foundational to longitudinal SEM design.\nAnalyze longitudinal panel models in both a single group and multi-group configuration in CFA and SEM framework.\nIncorporate mediation and moderation in a longitudinal framework.\nConstruct item parcels in a longitudinal framework.\nEvaluate latent growth curve models.\nApply latent growth curve models in a multivariate and multiple group context.\nEvaluate finite mixture models.\nInterpret and evaluate covariance pattern mixture models.\nEvaluate Growth mixture models.\nAddress missing data using FIML and MI methods.\nUse modern missing data treatments to implement a planned missing data design.\n\nSeminar Prerequisites\nSeminar Prerequisites:\nComing Soon… \nSoftware and Computer Support\nSoftware and Computer Support:\nComing Soon… \nSeminar Audience\nSeminar Audience:\nIf you already have a strong background in the application of SEM to analyze the covariance structure of multivariate data and you need to learn how to apply more advanced models to longitudinal data\, this seminar is for you. We strongly recommend that you attend our five-day intensive summer institute on the foundations of SEM as a pre-requisite to taking this five-day advanced seminar. If you have not taken the foundations Seminar\, you should have extensive experience or have taken a graduate-level seminar on SEM before enrolling. \nParticipants from a variety of fields\, including sociology\, psychology\, education\, human development\, marketing\, business\, biology\, medicine\, political science\, and communication\, will benefit from the seminar. \nThe seminar will support LISREL\, Mplus or Laavan. Some assistance will be available for questions related to other structural modeling packages. Previous knowledge of LISREL\, Mplus or Laavan is preferred but not required. \nSeminar Files\nSeminar Files\nInstructor will provide password on first day of seminar. \nAll statistical software used at Stats Camp will be available\, free to participants\, on our SMORS (statistical modeling on remote servers) system for the duration of camp.Syllabus\n\n\n\nSummer Stats Camp 2023: Longitudinal SEM\n\n\nMonday\nJune 12\, 2023\n\n\n9:00-10:45\nWelcome and Introductions. Overview of Longitudinal Models\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nDesign and Measurement Issues in Longitudinal Modeling\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:15\nReview of Foundations of SEM\n\n\n3:15-3:30\nRest Break\n\n\n3:30-5:00\nLongitudinal Panel Models: Basics\n\n\nTuesday\nJune 13\, 2023\n\n\n9:00-10:45\nMultiple-group Longitudinal Panel Models; CFA and SEM\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nParcels and Parceling\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:15\nLongitudinal Mediation & Moderation\n\n\n3:15-3:30\nRest Break\n\n\n3:30-5:00\nConsultation\n\n\nWednesday\nJune 14\, 2023\n\n\n9:00-10:45\nLatent Growth Curve Modeling: Basics\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nLatent Growth Curve Modeling: Multivariate and Multiple Groups\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:15\nIntroduction to Finite Mixture Modeling\n\n\n3:15-3:30\nRest Break\n\n\n3:30-5:00\nIntroduction to growth mixture modeling / Consultation\n\n\nThursday\nJune 15\, 2023\n\n\n9:00-10:45\nLatent class growth analysis (LCGA)\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nCovariance Pattern Mixture Models\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:15\nGrowth mixture modeling (GMM)\n\n\n3:15-3:30\nRest Break\n\n\n3:30-5:00\nGrowth mixture modeling (GMM)\n\n\nFriday\nJune 16\, 2023\n\n\n9:00-10:45\nMissing Data: Planned and Unplanned\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nWrap-up then Individual Consultations\n\n\n12:30-1:30\nRest Break\n\n\n1:30-~3:30\nIndividual Consultations\n\n\n\nDownload Sample Slides and Preview Course Video\nPlease fill out and submit the form below to get instant access to sample course materials.
URL:https://www.statscamp.org/courses/longitudinal-sem/
LOCATION:Embassy Suites – Albuquerque\, 1000 Woodward Place NE\, Albuquerque\, New Mexico\, 87102
CATEGORIES:Longitudinal SEM,Longitudinal Structural Equation Modeling,Summer Camp
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DTSTART;TZID=America/Denver:20230612T090000
DTEND;TZID=America/Denver:20230616T170000
DTSTAMP:20221127T103931
CREATED:20220701T093446Z
LAST-MODIFIED:20221114T220100Z
UID:2701-1686560400-1686934800@www.statscamp.org
SUMMARY:Multivariate Statistical Modeling using R
DESCRIPTION:IN PERSON – 5-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview:\nAn introductory 5-day course on using R software for common analytic methods in behavioral and social sciences. Topics covered include\, regression\, mediation and moderation\, multilevel modeling (MLM)\, factor analysis and structural equation modeling (SEM). \nSeminar Topics:\n\nIntroduction to R software and importing data into R\nFitting regression models in R\nTesting mediation and moderation models in R\nMLM in R\nFactor analysis and SEM in R\n\nSeminar Description:\nThis seminar is intended to introduce participants to popular multivariate statistical methods using the R software program. R is a free\, open-source software program which continues to grow in popularity across a wide variety of fields. R provides cutting edge functionality for most popular multivariate analyses used by researchers in behavioral and social sciences. \nThis seminar will help you begin to learn how to analyze multivariate models using R. The seminar will cover regression\, mediation\, moderation\, multilevel\, factor and SEM models in R. Using real datasets provided in the seminar\, participants will learn how to use the R software program to analyze data and interpret results. Further the seminar will focus on best practices approaches to model specification and interpretation across all covered methods. Coverage of confirmatory factor analysis and SEM will use the lavaan package. \nParticipants will receive an electronic copy of all course materials\, including lecture slides\, practice datasets\, software scripts\, relevant supporting documentation\, and recommended readings. Participants will also have access to a video recording of the course. \n\nInstructor: Alex Schoemann\, Ph.D.\n \nDr. Alexander M. Schoemann\, is an Associate Professor of Psychology at East Carolina University. Alex received his PhD from the University of Kansas in 2011 in Social and Quantitative Psychology under the mentorship of Dr. Kristopher Preacher. He has been a Stats Camp instructor since 2012 (after spending several years as a “counselor”). Alex teaches graduate courses in research design\, regression\, multivariate statistics\, structural equation modeling and multilevel modeling.… His research is focused on applying advanced quantitative methods to data from behavior sciences. Specific topics of interest include mediation and moderation\, power analyses\, missing data estimation\, meta-analysis\, structural equation models and multilevel models. Alex is also interested in developing user friendly software for advanced methods including applications for power analysis for mediation models (http://marlab.org/power_mediation/). \nRead More\n\n\nAPA Continuing Education Credits:\n \nThis course offers 29 hours of Continuing Education Credits. Stats Camp Foundation is approved by the American Psychological Association to sponsor continuing education for psychologists. Stats Camp Foundation maintains responsibility for this program and its content. \nSeminar Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\n\n\nLearning Objectives\nLearning Objectives:\nAfter engaging in course lectures and discussions as well as completing the hands-on practice activities with real data\, participants will be able to:\n\nAcquire an understanding of modeling techniques using R as applied in the educational\, social\, health\, and behavioral sciences\nSpecify\, estimate\, evaluate\, and compare regression models using R software\nSpecify\, estimate\, evaluate\, and compare mediation and moderation models using R software\nSpecify\, estimate\, evaluate\, and compare multilevel models using R software\nSpecify\, estimate\, evaluate\, and compare factor analysis and SEM models using R software\n\nParticipants will also complete the course with a foundation for future learning about statistical modeling with R and knowledge about available resources to guide such endeavors. \nSeminar Prerequisites\nSeminar Prerequisites:\nRequired: \n\nAdvanced proficiency in multiple linear regression\, including use of categorical independent variables.\nIntermediate fluency with statistical software (e.g. SAS\, SPSS\, or R) which will aid in the use of R (Note that materials for introducing attendees to R software will be shared in advance and the course will begin with a short introduction to R).\n\nNot required but advantageous: \n\nAt least limited experience (e.g.\, graduate-level course) with multivariate data analysis.\nAt least limited experience using R\n\nNo level of proficiency beyond basic awareness is assumed for skills related to: \n\nFactor Analysis\, SEM\, or MLM.\nAdvanced mathematical or statistical topics such as matrix algebra or likelihood theory.\n\nSoftware and Computer Support\nSoftware and Computer Support:\nRequired: \n\nAdvanced proficiency in multiple linear regression\, including use of categorical independent variables.\nIntermediate fluency with statistical software (e.g. SAS\, SPSS\, or R) which will aid in the use of R (Note that materials for introducing attendees to R software will be shared in advance and the course will begin with a short introduction to R).\n\nNot required but advantageous: \n\nAt least limited experience (e.g.\, graduate-level course) with multivariate data analysis.\nAt least limited experience using R\n\nNo level of proficiency beyond basic awareness is assumed for skills related to: \n\nFactor Analysis\, SEM\, or MLM.\nAdvanced mathematical or statistical topics such as matrix algebra or likelihood theory.\n\nSeminar Audience\nSeminar Audience:\nComing Soon… \nSeminar Files\nSeminar Files\n\nInstructor will provide password on first day of seminar. \n\nAll statistical software used at Stats Camp will be available\, free to participants\, on our SMORS (statistical modeling on remote servers) system for the duration of camp.Syllabus\n\n\n\nMonday\nJune 12\, 2023\n\n\n9:00-9:30\nWelcome and introductions\n\n\n9:30-10:45\nIntroduction to R\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nBasics of R and reading data into R\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nRegression with R\n\n\n3:00-3:15\nRest Break\n\n\n3:15-5:00\nRegression with R (continued)\n\n\nTuesday\nJune 13\, 2023\n\n\n9:00-10:45\nMediation with R\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nModeration with R\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nCombining Mediation and Moderation with R\n\n\n3:00-3:15\nRest Break\n\n\n3:15-5:00\nMissing Data Handling with R\n\n\nWednesday\nJune 14\, 2023\n\n\n9:00-10:45\nMLM with R\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nMLM with R (continued)\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nLongitudinal MLM with R\n\n\n3:00-3:15\nRest Break\n\n\n3:15-5:00\nLongitudinal MLM with R (continued)\n\n\nThursday\nJune 15\, 2023\n\n\n9:00-10:45\nExploratory Factor Analysis (EFA) with R\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nConfirmatory Factor Analysis (CFA) with R\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nMultiple group CFA with R\n\n\n3:00-3:15\nRest Break\n\n\n3:15-5:00\nMultiple group CFA with R (continued)\n\n\nFriday\nJune 16\, 2023\n\n\n9:00-10:45\nSEM with R\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nSEM with R\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nOne-on-one consultations with instructor\n\n\n3:00-3:15\nRest Break\n\n\n3:15-5:00\nOne-on-one consultations with instructor\n\n\n\nDownload Sample Slides and Preview Course Video\nPlease fill out and submit the form below to get instant access to sample course materials.
URL:https://www.statscamp.org/courses/multivariate-statistical-modeling-using-r/
LOCATION:Embassy Suites – Albuquerque\, 1000 Woodward Place NE\, Albuquerque\, New Mexico\, 87102
CATEGORIES:Multivariate Statistical Modeling using R,Summer Camp
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20230612T090000
DTEND;TZID=America/Chicago:20230616T170000
DTSTAMP:20221127T103931
CREATED:20220715T040118Z
LAST-MODIFIED:20221114T215955Z
UID:3321-1686560400-1686934800@www.statscamp.org
SUMMARY:Advanced Structural Equation Modeling: Bayesian SEM
DESCRIPTION:IN PERSON – 5-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview:\nYou have a good to great understanding of structural equation modeling (SEM)\, maybe you have been doing traditional SEM for years\, but you’ve began a project where your observations are not normally distributed\, you have a small sample size\, or even worse – your model is not converging! What do you do now? Bayesian SEM is your next step! \nAdvanced SEM: Bayesian SEM will cover models that may be too complicated or impossible to estimate in the traditional SEM framework. This seminar highlights the philosophical and practical advantages of the Bayesian approach to SEM. \nMaterial and examples will be provided using R\, but if you prefer to work with another program\, we will assist you to apply these methods in your program of choice. You can also request topics to be included during the weeklong seminar that may be developed into its own module! In addition\, personal consultation time will be available to help you propel your research forward. \nA perfect follow up for SEM Foundations or Bayesian Data Analysis! \n \nSeminar Topics:\n\nComing Soon…\n\nSeminar Description:\nYou have a good to great understanding of structural equation modeling (SEM)\, maybe you have been doing traditional SEM for years\, but you’ve began a project where your observations are not normally distributed\, you have a small sample size\, or even worse – your model is not converging! What do you do now? Bayesian SEM is your next step! \nAdvanced SEM: Bayesian SEM will cover models that may be too complicated or impossible to estimate in the traditional SEM framework. This seminar highlights the philosophical and practical advantages of the Bayesian approach to SEM. \n\nInstructor: Mauricio Garnier-Villarreal\, Ph.D.\n \nMauricio is a full time assistant professor at Vrije Universiteit Amsterdam. His Ph.D focused on Quantitative Psychology at the University of Kansas completed in the summer of 2016. His research focus is on the application of Bayesian methods to complex structure data for longitudinal analysis\, from both mixed-effects and SEM models. He has experience not only working in the test and development of methods\, but also in the application of these in data; in different fields like special education\, cognitive decline in aging\, healthy aging… (orcid.org/0000-0002-2951-6647). He has been involved in the Stats Camp since 2011. \nRead More\n\n\n \n\nInstructor: Esteban Montenegro\, Ph.D.\n \nI’m a researcher at the UC Davis Alzheimer’s Disease Center- East Bay. I conduct data analysis using advanced statistical methods such as latent variable modeling. I have 6 years of experience programming in R\, and I love learning about Linux and statistical new tools.…I’m always open to new projects and ideas\, I collaborate with several teams around the world on topics related to healthy aging\, Alzheimer Disease and other health related topics. \nRead More\n\n\n \n\nAPA Continuing Education Credits:\n \nThis course offers 29 hours of Continuing Education Credits. Stats Camp Foundation is approved by the American Psychological Association to sponsor continuing education for psychologists. Stats Camp Foundation maintains responsibility for this program and its content. \n\nSeminar Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\n\nLearning Objectives\nLearning Objectives:\nAfter engaging in course lectures and discussions as well as completing the hands-on practice activities with real data\, participants will be able to:\n\nDescribe the fundamental properties of Bayesian reasoning and Bayes’ rule.\nDifferentiate between direct probability and indirect probability.\nUtilize Markov Chain Monte Carlo estimation using the Gibbs and Hamiltonian samplers.\nAnalyze Bayesian models in several software packages\, including JAGS\, STAN\, and blavaan.\nIdentify the properties of key distributions used in Bayesian analysis.\nConduct a confirmatory factor analysis using the Bayesian framework.\nImplement prior estimations in conducting CFA modeling.\nEvaluate model fit and compare CFA models.\nModel multiple group CFAs using the Bayesian framework.\nEvaluate latent regression models\, latent interactions\, quadratic effects\, and mediation in a Bayesian framework.\nUse Bayesian methods to evaluate non-normal continuous data.\nConduct SEM for categorical data using Bayesian Item Response Theory.\nAccount for missing data.\nEvaluate longitudinal models in a Bayesian framework.\n\n \nSeminar Prerequisites\nSeminar Prerequisites:\nComing Soon… \nSoftware and Computer Support\nSoftware and Computer Support:\nComing Soon… \nSeminar Audience\nSeminar Audience:\nQ: Does this seminar focus on JAGS or STAN? \nA: I do cover Stan. The seminar doesn’t focus on writing either JAGS or Stan code\, since this is more complicated. \nThe seminar software focus on the use of blavaan (user friendly)\, which runs the analysis for you in either JAGS or Stan (your choice). For special cases of models\, we go over how to edit the code (JAGS or Stan) to run models that blavaan doesnt include yet. \nIf there is the interest from a student to go over more detail Stan code we can do that\, there is time to adjust the seminar to the students needs\, or work on that during consultation. \nSeminar Files\nSeminar Files\nInstructor will provide password on first day of seminar. \nAll statistical software used at Stats Camp will be available\, free to participants\, on our SMORS (statistical modeling on remote servers) system for the duration of camp.Syllabus\n\n\n\nSummer Stats Camp 2023: Bayesian SEM\n\n\nMonday\nJune 12\, 2023\n\n\n9:00–10:45\nWelcome and Introductions.\n\n\n10:45–11:00\nRest Break\n\n\n11:00–12:30\nBayesian reasoning and Baye’s rule : Direct probability vs indirect probability\n\n\n12:30–1:30\nRest Break\n\n\n1:30–3:15\nMarkov Chain Monte Carlo Estimation: Gibbs and Hamiltonian samplers\n\n\n3:15–3:30\nRest Break\n\n\n3:30–5:00\nIntroduction to R: blavaan\, JAGS\, STAN\n\n\nTuesday \nJune 13\, 2023\n\n\n9:00–10:45\nKnow your distributions\n\n\n10:45–11:00\nRest Break\n\n\n11:00–12:30\nConfirmatory Factor Analysis\n\n\n12:30–1:30\nRest Break\n\n\n1:30–3:15\nPriors: relevance\, choice\, strengths. CFA with cross-loadings\n\n\n3:15–3:30\nRest Break\n\n\n3:30–5:00\nIndividual Consultations\n\n\nWednesday\nJune 14\, 2023\n\n\n9:00–10:45\nModel fit and model comparison\n\n\n10:45–11:00\nRest Break\n\n\n11:00–12:30\nMultiple-group CFA\n\n\n12:30–1:30\nRest Break\n\n\n1:30–3:15\nLatent regression models: latent interactions\, and mediation\n\n\n3:15–3:30\nRest Break\n\n\n3:30–5:00\nIndividual Consultations.\n\n\nThursday\nJune 15\, 2023\n\n\n9:00–10:45\nSEM for categorical data: Item Response Theory\n\n\n10:45–11:00\nRest Break\n\n\n11:00–12:30\nMissing data handling\n\n\n12:30–1:30\nRest Break\n\n\n1:30–3:15\nLongitudinal CFA (Measurement Invariance)\n\n\n3:15–3:30\nRest Break\n\n\n3:30–5:00\nIndividual Consultations.\n\n\nFriday\nJune 16\, 2023\n\n\n9:00–10:45\nLatent Growth Curve models\n\n\n10:45–11:00\nRest Break\n\n\n11:00–12:30\nLatent Change Scores\n\n\n12:30–1:30\nRest Break\n\n\n1:30~3:30\nIndividual Consultations.\n\n\n\n \n \nDownload Sample Slides and Preview Course Video\nPlease fill out and submit the form below to get instant access to sample course materials.
URL:https://www.statscamp.org/courses/advanced-structural-equation-modeling-bayesian-sem/
LOCATION:Embassy Suites – Albuquerque\, 1000 Woodward Place NE\, Albuquerque\, New Mexico\, 87102
CATEGORIES:Advanced Structural Equation Modeling: Bayesian SEM Seminar,Summer Camp
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DTSTART;TZID=America/Chicago:20230612T090000
DTEND;TZID=America/Chicago:20230616T170000
DTSTAMP:20221127T103931
CREATED:20220715T042349Z
LAST-MODIFIED:20221114T215912Z
UID:3330-1686560400-1686934800@www.statscamp.org
SUMMARY:Mediation and Moderation
DESCRIPTION:IN PERSON – 5-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview:\nGreat! You have an idea you’re interested in\, now what? You may even have a theory that X will predict Y\, but the more important question\, the question that we really what to know is\, why does X predict Y\, or when does X predict Y. Modeling these mechanisms of change are where Mediation and/or Moderation become your methodological hero! \nSeminar Topics:\n\nComing Soon…\n\nSeminar Description:\nOur Mediation and Moderation seminar will teach you the fundamentals of framing the relationships between your observations\, including how to: \n\nEstimate\, test\, and interpret mediated (i.e.\, indirect) and moderated (i.e.\, interaction) effects using OLS regression.\nCombine mediation and moderation models to test conditional indirect effects.\nUse a macro called PROCESS for SPSS and SAS to test these models.\nApply the latest methods in moderation and mediation analysis using R software.\n\n\nInstructor: Mwarumba Mwavita\, Ph.D.\n \nMwarumba Mwavita is Director of the Center for Educational Research and Evaluation (CERE) at Oklahoma State University. In addition\, he is an Assistant Professor in the Research\, …Evaluation\, Measurement and Statistics (REMS) program in the School of Educational Studies (SES). \nRead More\n\n\n\n \n\nAPA Continuing Education Credits:\n \nThis course offers 29 hours of Continuing Education Credits. Stats Camp Foundation is approved by the American Psychological Association to sponsor continuing education for psychologists. Stats Camp Foundation maintains responsibility for this program and its content. \n\nSeminar Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\n\nLearning Objectives\nLearning Objectives:\nThe five-day training institute on Mediation and Moderation will enable participants to:\n\nCompute\, test\, and interpret mediation models in an OLS regression framework.\nEvaluate mediation models with four or more variables.\nImplement latent variable mediation.\nImplement mediation in a multilevel framework.\nEvaluate mediation in a longitudinal framework.\nCompute\, test\, interpret\, and graph moderation in an OLS regression framework.\nImplement moderation in a latent variable framework.\nImplement moderation in a multilevel framework.\nEvaluate advanced models that implement moderated mediators\, or mediated moderation.\n\nSeminar Prerequisites\nSeminar Prerequisites:\nRequired: \n\nIntermediate proficiency in multiple linear regression\nIntermediate proficiency with at least once statistical software package (knowledge of SPSS or SAS are preferred\, but not required).\nAt least limited experience using syntax with statistical software.\n\nNot required but advantageous: \n\nAt least limited experience (e.g.\, graduate-level seminar) with multivariate data analysis.\nAt least limited experience (e.g.\, graduate-level seminar) with investigating interactions (in r multiple linear regression or ANOVA)\n\nNo level of proficiency beyond basic awareness is assumed for skills related to: \n\nMediation and moderation\nAdvanced statistical techniques such as SEM\, MLM\, or logistic regression.\nAdvanced mathematical or statistical topics such as matrix algebra or likelihood theory.\n\nSoftware and Computer Support\nSoftware and Computer Support:\nParticipants need to bring a laptop computer with Wi-Fi capabilities. \nAll statistical software used at Stats Camp will be available\, free to participants\, on our SMORS (statistical modeling on remote servers) system for the duration of camp. \nInstruction will be provided for the methods using the most current version of the PROCESS macro (processmacro.org). PROCESS is freely available as a macro for SPSS and SAS. SPSS and SAS are available for Windows\, Mac\, and Linux environments. \nWe shall use SPSS software for this seminar. \nSeminar Audience\nSeminar Audience:\nThis seminar will be helpful for researchers in any field—including psychology\, sociology\, education\, business\, human development\, political science\, public health\, communication—and others who want to learn how to apply the latest methods in moderation and mediation analysis using freely-available R software. Participants should have a good working knowledge of the principles and practice of multiple regression and elementary statistical inference. \nSeminar Files\nSeminar Files\nInstructor will provide password on first day of seminar. \nAll statistical software used at Stats Camp will be available\, free to participants\, on our SMORS (statistical modeling on remote servers) system for the duration of camp.Syllabus\n \n\n\n\nSummer Stats Camp 2023: Mediation Moderation\n\n\nMonday\nJune 12\, 2023\n\n\n9:00-10:45\nWelcome and software review\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nReview of regression\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:15\nIntroduction to mediation\n\n\n3:15-3:30\nRest Break\n\n\n3:30-5:00\nComputing\, testing and interpreting mediation in regression\n\n\nTuesday\nJune 13\, 2023\n\n\n9:00-10:45\nMediation with four or more variables\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nLatent variable mediation\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:15\nAdvanced topics in mediation: Mulilevel modeling\n\n\n3:15-3:30\nRest Break\n\n\n3:30-5:00\nAdvanced topics in mediation: Longitudinal mediation\n\n\nWednesday\nJune 14\, 2023\n\n\n9:00-10:45\nIntroduction to moderation\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nComputing moderation\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:15\nGraphing and interpreting moderation\n\n\n3:15-3:30\nRest Break\n\n\n3:30-5:00\nGraphing and interpreting moderation in regression / Individual Consultations\n\n\nThursday\nJune 15\, 2023\n\n\n9:00-10:45\nAdvanced topics in moderation: Multilevel modeling\, SEM\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nAdvanced topics in Moderation: Longitudinal designs\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:15\nAdvance topics in moderation\n\n\n3:15-3:30\nRest Break\n\n\n3:30-5:00\nContinue Advanced topics / Individual Consultations\n\n\nFriday\nJune 16\, 2023\n\n\n9:00-10:45\nCombining mediation and moderation\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nTesting and interpreting conditional indirect effects\n\n\n12:30-1:30\nRest Break\n\n\n1:30-~3:30\nIndividual Consultations\n\n\n\n \nDownload Sample Slides and Preview Course Video\nPlease fill out and submit the form below to get instant access to sample course materials.
URL:https://www.statscamp.org/courses/mediation-and-moderation-5day/
LOCATION:Embassy Suites – Albuquerque\, 1000 Woodward Place NE\, Albuquerque\, New Mexico\, 87102
CATEGORIES:Mediation and Moderation,Summer Camp
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DTSTART;TZID=America/Chicago:20230612T090000
DTEND;TZID=America/Chicago:20230616T170000
DTSTAMP:20221127T103931
CREATED:20220715T043322Z
LAST-MODIFIED:20221114T215811Z
UID:3336-1686560400-1686934800@www.statscamp.org
SUMMARY:Multilevel SEM with xxM
DESCRIPTION:IN PERSON – 5-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview:\nThis seminar teaches skills necessary to conduct analysis of complex multilevel data-structures using xxM from an n-Level Structural Equation Modeling (NL-SEM) perspective. The n-Level structural equation modeling framework is compatible for both conventional and non-standard data-structures. Currently aspects of such non-standard data can be handled within most MLM or ML-SEM packages\, but do not scale well with increasingly complex data-structures. \nSeminar Topics:\n\nComing Soon…\n\nSeminar Description:\nThe innovative software package xxM provides a broader\, simpler conceptual framework to match the consistent jargon-free language within the NL-SEM framework. All manner of models for nested data structures are easily specified and estimated using xxM\, which is a free software program developed for the R platform.\nThis seminar is designed to introduce participants to the modeling mindset of the NL-SEM framework and gain ample experience with the xxM software package. \nThe perfect follow up for those who have taken our SEM Foundations & Extended Applications Course. \n\nInstructor: Paras Mehta\, Ph.D.\nParas is an Associate Professor of Clinical Psychology and Industrial Organizational Psychology at the University of Houston.… His research interests include multilevel structural equations modeling\, growth curve modeling\, and applications of ML-SEM in educational and organizational research. \nRead More\n\n \n\n\nAPA Continuing Education Credits:\n \nThis course offers ? hours of Continuing Education Credits. Stats Camp Foundation is approved by the American Psychological Association to sponsor continuing education for psychologists. Stats Camp Foundation maintains responsibility for this program and its content. \n\nSeminar Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\n\nLearning Objectives\nLearning Objectives:\nAfter engaging in course lectures and discussions as well as completing the hands-on practice activities with real data\, participants will be able to:\n\nUnderstand the ‘big-data’ nature of multilevel\, latent variable modeling.\nSee the ‘big picture’ view of multilevel modeling from a single unified NL-SEM framework in which SEM and MLM are the simple building blocks.\nUnderstand how different types of complex data-structures imply dependency among observed data.\nDevelop the skills necessary to translate complex multilevel data-structures and the corresponding multilevel hypotheses into a statistical model.\nBecome proficient in the use of xxM for fitting multilevel latent variable models.\nBecome acquainted with a variety of novel and useful multilevel models in education\, business and social psychology.\nThink of study designs within your area of research that can answer more interesting and novel research questions.\nConduct multilevel modeling with xxM using random intercepts\, contextual-effects\, and centering.\nEvaluate multilevel cross-classified data\, an emerging class of contextual SEM.\nConduct multilevel measurement invariance across multiple groups.\nEvaluate complex data structures\, such as longitudinal\, cross-classified\, and multiple membership data.\nEvaluate longitudinal data with switching classification.\nImplement a social relations model with reciprocal ratings in round-robin designs and 360 evaluations.\n\n \nSeminar Prerequisites\nExample Data Structures:\n\nLongitudinal data with switching classification in which nesting of a lower- level unit within the higher-level unit changes over time. For example\, students switch teachers and classrooms across grades.\nMultiple ratings over time (longitudinal data with cross-classification and multiple membership). For example\, each student may be rated by multiple different teachers and each teacher may rate multiple different students on multiple occasions. Such data are common in business settings as well.\nNon-hierarchical teams. Each person may belong to multiple teams over time. Examples of such data are common in business settings. Other examples include patients treated by teams of doctors and nurses.\nDependent variables at “multiple lower levels.” For example\, in a health-setting context we may have outcome variables for patients (satisfaction)\, nurses (job-satisfaction) and doctors (stress) over time\, and nesting of patients within nurses and doctors may not be hierarchical.\nRound-robin data where each person may rate multiple other individuals within a small group. Variants of such data are common in business & I/O psychology (360 evaluations\, team-performance)\, social-psychology (person perceptions) and education (peer-evaluations).\nPartially nested data. Only a subset of subjects are nested within a higher level unit. Such data occur commonly in “intervention studies” (medical\, educational etc.) where individual subjects are randomly assigned to intervention and control conditions. Intervention itself is delivered in small groups leading to clustering; however\, control subjects are not nested.\nData with multiple different types of dependencies and many levels. For example\, large longitudinal state datasets with multiple cohorts of students nested within teachers\, classrooms\, schools and districts. Classification may change across grades.\n\nSoftware and Computer Support\nSoftware and Computer Support:\nComing Soon… \nSeminar Audience\nSeminar Audience:\nThis seminar may be thought of an introductory-advanced seminar on Multilevel Structural Equation Modeling. The seminar assumes basic knowledge of MLM and SEM. A graduate seminar or a five-day workshop in SEM and MLM is probably necessary. That said\, all essential concepts of SEM and MLM will be reviewed and re-introduced from a unified perspective during the first two days. \nNovice participants with limited experience in MLM or SEM can expect to learn key ideas behind the modeling framework and learn how to fit fairly complex models using xxM. Expert participants will acquire a deeper understanding of how SEM and MLM may be readily and simply extended to \nParticipants are encouraged to contact the instructor before the workshop with their own data and research questions. \nParticipants from a variety of fields\, including psychology\, education\, human development\, sociology\, marketing\, business\, biology\, medicine\, political science\, and communication\, will benefit from the seminar. \nSeminar Files\nSeminar Files\nInstructor will provide password on first day of seminar. \nAll statistical software used at Stats Camp will be available\, free to participants\, on our SMORS (statistical modeling on remote servers) system for the duration of camp.Syllabus\nThe first two days will re-introduce conventional Structural Equation Modeling (SEM) and Multilevel Modeling (MLM) from a unified nLevel SEM perspective. \nThe last three days will focus on models of complex multilevel data-structures that are often difficult to conceptualize within conventional MLM or ML-SEM frameworks. Real-world examples of complex data and challenging research questions will be used to teach principles and practice of multilevel SEM. The approach will be practical with an emphasis on model fitting and interpretation of results. \n\n\n\nSummer Stats Camp 2023: Multilevel SEM with xxM\n\n\nDay 1\n\n\n\n\n9:00 – 9:30\nWelcome and Introductions\n\n\n10:45 – 11:00\nRest Break\n\n\n11:00 -12:30\nBuilding blocks: Single level SEM in xxM\n\n\n12:30 – 1:30\nRest Break\n\n\n1:30 – 3:15\nConfirmatory Factor Analysis\n\n\n3:15 – 3:30\nRest Break\n\n\n3:30 – 5:00\nMultiple-Group CFA\n\n\nDay 2\n\n\n\n\n9:00 – 10:45\nBuilding blocks: Multilevel Modeling in xxM\n\n\n10:45 – 11:00\nRest Break\n\n\n11:00 -12:30\nRandom intercepts model\, contextual-effects and centering\n\n\n12:30 – 1:30\nRest Break\n\n\n1:30 – 3:15\nRandom-slopes model\n\n\n3:15 – 3:30\nRest Break\n\n\n3:30 – 5:00\nIndividual Consultations\n\n\nDay 3\n\n\n\n\n9:00 – 10:45\nN-Level Structural Equations Modeling: Data-structures\n\n\n10:45 – 11:00\nRest Break\n\n\n11:00 -12:30\nMultivariate cross-classified data: Emerging class of contextual SEM\n\n\n12:30 – 1:30\nRest Break\n\n\n1:30 – 3:15\nMultilevel measurement invariance: Multiple groups model\n\n\n3:15 – 3:30\nRest Break\n\n\n3:30 – 5:00\nIndividual Consultations\n\n\nDay 4\n\n\n\n\n9:00 – 10:45\nLatent Growth Curves: Long and Wide format\n\n\n10:45 – 11:00\nRest Break\n\n\n11:00 -12:30\nComplex data structures: Longitudinal\, Cross-classified and Multiple membership data\n\n\n12:30 – 1:30\nRest Break\n\n\n1:30 – 3:15\nComplex data structures: Longitudinal data with switching classification\n\n\n3:15 – 3:30\nRest Break\n\n\n3:30 – 5:00\nIndividual Consultations\n\n\nDay 5\n\n\n\n\n9:00 – 10:45\nPartially nested data: Issues and models\n\n\n10:45 – 11:00\nRest Break\n\n\n11:00 -12:30\nSocial Relations Model: Reciprocal ratings in round-robin designs/360 evaluation\n\n\n12:30 – 1:30\nRest Break\n\n\n1:30 – 4:30\nIndividual Consultations\n\n\n\n \nDownload Sample Slides and Preview Course Video\nPlease fill out and submit the form below to get instant access to sample course materials.
URL:https://www.statscamp.org/courses/multilevel-sem-with-xxm/
LOCATION:Embassy Suites – Albuquerque\, 1000 Woodward Place NE\, Albuquerque\, New Mexico\, 87102
CATEGORIES:Multilevel SEM with xxM,Summer Camp
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DTSTART;TZID=America/Chicago:20230612T090000
DTEND;TZID=America/Chicago:20230616T170000
DTSTAMP:20221127T103931
CREATED:20220715T045043Z
LAST-MODIFIED:20221114T213953Z
UID:3343-1686560400-1686934800@www.statscamp.org
SUMMARY:SEM with Mplus
DESCRIPTION:IN PERSON – 5-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview:\nAn introductory 5-day training course on using Mplus for SEM. \nMore and more researchers in the social and behavioral sciences use\, or want to use\, Mplus to analyze their structural equation models. This Stats Camp course is a 5-day hands on workshop using Mplus. \nSeminar Topics:\nThe five-day training institute on SEM with Mplus will enable participants to: \n\nBrief review of SEM\nPreparing and reading data into Mplus\nModel specification and dealing with defaults\nProcedures for fitting and testing SEM models (regression\, path analysis\, multiple groups\, moderation ) in Mplus\nTesting mediation (bootstrapping)\nHow to fit longitudinal models (Panel vs Growth curve models)\n\nNote: This course will focus primarily on multivariate normal data that meets the assumptions of maximum likelihood estimation although other estimators available in Mplus will be briefly discussed. \nSeminar Description:\nThe course starts with a brief review of structural equation modeling with emphasis on the specific way Mplus is used to specify and estimate models. We will also discuss some ways to deal with warnings and error messages. Next\, we work with model specification and comparisons\, multigroup models\, DIF testing\, moderation\, and how to deal with Mplus defaults. We continue with testing predictive hypotheses and mean differences. Then\, we will cover more advanced topics related to longitudinal data along with its additional assumptions and how to fit longitudinal SEM models in Mplus (e.g. how to specify longitudinal panel models\, growth curve models\, and how to test for mediation). The last day provides time for an additional topic based on camper requests (i.e. MLM\, Power Analysis\, etc.). The available choices of different estimation methods and statistical tests are also discussed. \nOn each day\, the morning session consists of mini-lectures and examples\, and the afternoon session is a computer lab where the topics of the morning are applied on example data. There will also be time to work on your own data and get feedback on your models. Bringing your own data is a plus for you\, but definitely not a requirement for this course as we will have plenty of examples. \nParticipants from a variety of fields—including psychology\, education\, human development\, public health\, prevention science\, sociology\, marketing\, business\, biology\, medicine\, political science\, and communication—will benefit from the course. \nOn the first day of class you will receive an electronic copy of all course materials\, including lecture slides\, practice datasets\, software scripts\, relevant supporting documentation\, and recommended readings. You will also have access to a video recording of the course. \n\nInstructor: Elizabeth Grandfield\, Ph.D.\n \nElizabeth received her Ph.D. in Quantitative Psychology at the University of Kansas. She is currently an Assistant Professor in the Department of Methodology and Statistics at Utrecht University in the Netherlands. Her research focuses on evaluating measurement invariance with an emphasis in longitudinal designs. In areas of applied research\, Elizabeth has been involved in longitudinal children studies at Juniper Gardens as well as a national nursing study at Kansas University Medical Center\, both in Kansas City. She also received… the 2011 Multivariate Software Award\, presented by Peter Bentler and Eric Wu. Elizabeth has been involved in Stats Camp since 2012. \nRead More\n\n\n \n\n\nAPA Continuing Education Credits:\n \nThis course offers ? hours of Continuing Education Credits. Stats Camp Foundation is approved by the American Psychological Association to sponsor continuing education for psychologists. Stats Camp Foundation maintains responsibility for this program and its content. \n\nSeminar Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\n\nLearning Objectives\nLearning Objectives:\nAfter engaging in course lectures and discussions as well as completing the hands-on practice activities\, participants will:\n\nAcquire an understanding of how Mplus syntax is structured and used to analyze structural equation models.\nEvaluate and respond to common error messages generated by Mplus.\nSet the scale in an SEM or CFA model in Mplus by overriding the program defaults for marker variable\, fixed factor\, and effects coding scaling methods.\nPrepare data for input into Mplus.\nFit CFA models using Mplus.\nConduct model comparisons and invariance testing using Mplus.\nEvaluate structural invariance in predictive models.\nEvaluate panel models and growth curve models using Mplus.\nImplement longitudinal designs\, mediation\, and bootstrapping in Mplus.\n\nSeminar Prerequisites\nSeminar Prerequisites:\nRequired: \n\nIntermediate proficiency in multiple linear regression (e.g.\, a second course in statistics is usually sufficient).\nIntermediate proficiency with at least one statistical software package (e.g.\, SPSS\, Stata\, SAS\, R\, etc.).\nAt least limited experience (or basic awareness) with continuous latent variable models\, e.g.\, exploratory and confirmatory factor analysis (EFA; CFA) and structural equation modeling (SEM).\nPlease email the instructor if you have any questions or concerns. Some introductory literature/articles can be recommended/provided before the course begins.\n\nNot required but advantageous: \n\nAt least limited experience with multivariate data analysis.\n\nNo level of proficiency beyond basic awareness is assumed for skills related to: \n\nLatent variable modeling using Mplus.\nAdvanced mathematical or statistical topics such as matrix algebra or likelihood theory.\n\nSoftware and Computer Support\nSoftware and Computer Support:\nParticipants need to bring a laptop computer with Wi-Fi capabilities. \nAll statistical software used at Stats Camp will be available\, free to participants\, on our SMORS (statistical modeling on remote servers) system for the duration of camp. \nInstruction will be provided for the methods using the most current version of Mplus (base program with mixture add-on or base program with combination add-on). Mplus is available for Windows\, Mac\, and Linux environments. Information for purchasing a personal license can be found at www.statmodel.com. \nSeminar Audience\nSeminar Audience:\nIf you need to analyze your data in Mplus or if you want to know when to switch to Mplus\, this seminar is for you. You should have some (basic) experience with other SEM software\, for example AMOS\, LISREL\, openMX\, SAS. No previous knowledge of Mplus is assumed. You do not need to know matrix algebra\, calculus\, or likelihood theory\, or any knowledge on Bayesian statistics. Participants from a variety of fields\, including sociology\, psychology\, education\, human development\, marketing\, business\, biology\, medicine\, political science\, and communication\, will benefit from the seminar. \nSeminar Files\nSeminar Files\nInstructor will provide password on first day of seminar. \nAll statistical software used at Stats Camp will be available\, free to participants\, on our SMORS (statistical modeling on remote servers) system for the duration of camp.Syllabus\n\n\n\nMonday\nJune 12\, 2023\n\n\n9:00-9:30\nWelcome and introductions\n\n\n9:30-10:45\nSEM: Brief Review\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nIntro to Mplus syntax and common error messages\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nScale setting in CFA/SEM (overriding Mplus defaults)\n\n\n3:00-3:15\nRest Break\n\n\n3:15-5:00\nComputer exercises and getting your data ready for Mplus\n\n\nTuesday\nJune 13\, 2023\n\n\n9:00-10:45\nQ&A\, Fitting CFA models\, Model comparisons\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nIntro to Invariance testing (Multiple group models and moderation)\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nMplus examples\n\n\n3:00-3:15\nRest Break\n\n\n3:15-5:00\nComputer exercises\n\n\nWednesday\nJune 16\, 2023\n\n\n9:00-10:45\nQ&A and catch up\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nStructural Invariance\, predictive models\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nMplus examples\, exercises\n\n\n3:00-3:15\nRest Break\n\n\n3:15-5:00\nComputer exercises and camper hypothesized models\n\n\nThursday\nJune 15\, 2023\n\n\n9:00-10:45\nQ&A and catch up\, longitudinal SEM\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nPanel vs growth curve model specification in Mplus\n\n\n12:30-1:30\nRest Break\n\n\n1:30-3:00\nMore on longitudinal\, mediation\, bootstrapping in Mplus\n\n\n3:00-3:15\nRest Break\n\n\n3:15-5:00\nComputer exercises and working with your own data\n\n\nFriday\nJune 16\, 2023\n\n\n9:00-10:45\nQ&A and catch up\n\n\n10:45-11:00\nRest Break\n\n\n11:00-12:30\nMisc topic (based on camper response to survey)\n\n\n12:30-1:30\nRest Break\n\n\n1:30 ~ 3:00\nOne-on-one consultations with instructor\n\n\n\nDownload Sample Slides and Preview Course Video\nPlease fill out and submit the form below to get instant access to sample course materials.
URL:https://www.statscamp.org/courses/sem-with-mplus/
LOCATION:Embassy Suites – Albuquerque\, 1000 Woodward Place NE\, Albuquerque\, New Mexico\, 87102
CATEGORIES:SEM with Mplus,Summer Camp
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20231116T080000
DTEND;TZID=America/Chicago:20231119T170000
DTSTAMP:20221127T103931
CREATED:20221014T045552Z
LAST-MODIFIED:20221114T213823Z
UID:4647-1700121600-1700413200@www.statscamp.org
SUMMARY:Network Psychometrics
DESCRIPTION:LIVE STREAM – 4-day Statistics Short Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nSeminar Overview:\nAn introductory 4-day course in the application of psychometrics. Participants should be proficient specific to the material covered in a two-semester graduate-level social science statistics course sequence. \nSeminar Topics:\n\nMeasurement and statistical concepts specific to psychometrics\nScaling\, scaling models & scale development – stimulus\, response and subject centered\nValidity – conceptual and statistical aspects necessary for evidential arguments\nIntroduction to Factor analysis – traditional\, IRT and SEM-based approaches/connections\nReliability – classical and modern approaches to estimation of score reliability\nIntroduction to Item Response Theory\n\nSeminar Description:\nPsychometrics is defined as the science of evaluating the characteristics of tests or other devices designed to measure psychological attributes of people. Tests are broadly defined as devices for measuring ability\, aptitude\, achievement\, attitudes\, interests\, personality\, cognitive functioning\, and mental health. Application of psychometrics to psychology and social/behavioral science constitutes an organized effort to (a) properly use theory-based measurement procedures for the development of tests and other measurement instruments for inter- and intraindividual research designs and (b) incorporate current best practices for applying measurement theory\, item/scale development\, reliability estimation (classical and modern)\, factor analysis/IRT and establishing statistical evidence of score validity through a unified approach. advance knowledge in psychological and sensory processes. Participants will receive an electronic copy of all course materials\, including lecture slides\, practice datasets\, software scripts\, relevant supporting documentation\, and recommended readings. Participants will also have access to a video recording of the course. \n\nInstructor: Larry Price\, Ph.D.\n \nLarry Price\, Ph.d. PStat is the Director of the Data Analytics & Methodology at Texas State University. This university-wide role involves supervising a team of quantitative methodologists in conceptualizing and writing the analytic segments of large-scale competitive grant proposals for funding agencies such as the Department of Education/Institute of Education Sciences\, National Science Foundation\, National Institutes of Health\, National Institute on Standards\, and the Department of Defense in collaboration with interdisciplinary research teams. His research has been funded by agencies such as the Department of Education/Institute of Education Sciences\, National Science Foundation\, National Institutes of Health\, National Institute on Standards\, and the Department of Defense. \nRead More\n\n\nAPA Continuing Education Credits:\n \nThis course offers ? hours of Continuing Education Credits. Yhat Enterprises\, LLC is approved by the American Psychological Association to sponsor continuing education for psychologists. Yhat Enterprises\, LLC maintains responsibility for this program and its content. \nSeminar Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\nLearning Objectives\nLearning Objectives:\n\nAcquire a basic understanding of the role of psychometrics as applied to social and behavioral sciences.\nDevelop a clear understanding of the conceptual and theoretical basis of measurement and statistical concepts specific to psychometrics.\nAcquire knowledge of how to properly apply psychometric techniques such as scale development\, item analysis/refinement\, score reliability and statistical validity.\nGain knowledge of how to apply factor analysis using traditional and structural equation modeling approaches.\nGain knowledge of how to apply generalizability theory for estimating variance components and score reliability when classical test theory model is inadequate.\nAcquire basic knowledge of how and why to apply item response theory for scaling test data.\n\nSeminar Prerequisites\nSeminar Prerequisites:\nRequired: \n\nIntermediate proficiency in basic statistical theory as would be gained in a 1st year graduate course.\n\nNot required but advantageous: \n\nLimited experience (e.g.\, graduate-level course) with classical measurement theory and concepts.\n\nNo level of proficiency beyond basic awareness is assumed for skills related to: \n\nAdvanced mathematical or statistical topics such as matrix algebra.\n\nSoftware and Computer Support\nSoftware and Computer Support:\nParticipants need to bring a laptop computer with Wi-Fi capabilities. Students should have access to IBM SPSS\, version 21.0 or higher and Mplus\, version 7.1 or higher and R. \nAll statistical software used at Stats Camp will be available\, free to participants\, on our SMORS (statistical modeling on remote servers) system for the duration of camp.Syllabus\n\n\n\nDay 1\n\n\n\n\n9:00-9:30\nWelcome and introductions\n\n\n9:30-10:30\nMeasurement & statistical concepts\n\n\n10:30-10:45\nSnack and refreshment break\n\n\n10:45-12:30\nScaling and scaling models – achievement\, ability\, attitude & perception\n\n\n12:30-1:30\nLunch break\n\n\n1:30-3:00\nTechniques for item and test development\, evaluation & refinement\n\n\n3:00-3:15\nSnack and refreshment break\n\n\n3:15-5:00\nValidity – criterion\, content & construct considerations\n\n\nDay 2\n\n\n\n\n9:00-10:45\nStatistical aspects of the score validation process\n\n\n10:45-11:00\nSnack and refreshment break\n\n\n11:00-12:30\nFactor analysis – foundations\, types and estimating factor models using exploratory and confirmatory approaches – part 1\n\n\n12:30-1:30\nLunch break\n\n\n1:30-3:00\nFactor analysis – a unified model for test theory and application\, estimating factor models using structural equation modeling – part 2\n\n\n3:00-3:15\nSnack and refreshment break\n\n\n3:15-5:00\nIndividual Consultations (alternatively start reliability presentation)\n\n\nDay 3\n\n\n\n\n9:00-10:45\nReliability of test scores – foundations/application of classical test theory; Using/applying structural equation modeling and IRT for score reliability estimation\n\n\n10:45-11:00\nSnack and refreshment break\n\n\n11:00-12:30\nIntroduction to Item Response Theory – theory and applications for applied psychometrics; Relationship to structural equation modeling\n\n\n12:30-1:30\nLunch break\n\n\n1:30-3:00\nIntroduction to generalizability theory\n\n\n3:00-3:15\nSnack and refreshment break\n\n\n3:15-5:00\nIndividual Consultations (or continue presentation of material)\n\n\n\n \nDownload Sample Slides and Preview Course Video\nPlease fill out and submit the form below to get instant access to sample course materials.
URL:https://www.statscamp.org/courses/network-psychometrics-2/
LOCATION:Livestream and/or Asynchronous:\, If you are unavailable to join live via Zoom\, you can participate asynchronously by viewing the recorded course videos for up to 1 year.
CATEGORIES:Network Psychometrics,Psychometrics
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